Lex Fridman Podcast - #111 - Richard Karp: Algorithms and Computational Complexity

The following is a conversation with Richard Karp,

a professor at Berkeley and one of the most important figures

in the history of theoretical computer science.

In 1985, he received the Turing Award

for his research in the theory of algorithms,

including the development of the Admirons Karp algorithm

for solving the max flow problem on networks,

Hopcroft Karp algorithm for finding maximum cardinality

matchings in bipartite graphs,

and his landmark paper in complexity theory

called Reduceability Among Combinatorial Problems,

in which he proved 21 problems to be NP complete.

This paper was probably the most important catalyst

in the explosion of interest in the study of NP completeness

and the P versus NP problem in general.

Quick summary of the ads.

Two sponsors, 8sleep mattress and Cash App.

Please consider supporting this podcast

by going to 8sleep.com slash Lex

and downloading Cash App and using code LexPodcast.

Click the links, buy the stuff.

It really is the best way to support this podcast.

If you enjoy this thing, subscribe on YouTube,

review it with five stars on Apple Podcast,

support it on Patreon,

or connect with me on Twitter at Lex Friedman.

As usual, I’ll do a few minutes of ads now

and never any ads in the middle

that can break the flow of the conversation.

This show is sponsored by 8sleep and its Pod Pro mattress

that you can check out at 8sleep.com slash Lex

to get $200 off.

It controls temperature with an app.

It can cool down to as low as 55 degrees

on each side of the bed separately.

Research shows that temperature has a big impact

on the quality of our sleep.

Anecdotally, it’s been a game changer for me.

I love it.

It’s been a couple of weeks now.

I’ve just been really enjoying it,

both in the fact that I’m getting better sleep

and that it’s a smart mattress, essentially.

I kind of imagine this being the early days

of artificial intelligence being a part

of every aspect of our lives.

And certainly infusing AI in one of the most important

aspects of life, which is sleep,

I think has a lot of potential for being beneficial.

The Pod Pro is packed with sensors that track heart rate,

heart rate variability, and respiratory rate,

showing it all in their app.

The app’s health metrics are amazing,

but the cooling alone is honestly worth the money.

I don’t always sleep, but when I do,

I choose the 8th Sleep Pod Pro mattress.

Check it out at 8thSleep.com slash Lex to get $200 off.

And remember, just visiting the site

and considering the purchase helps convince the folks

at 8th Sleep that this silly old podcast

is worth sponsoring in the future.

This show is also presented by the great

and powerful Cash App,

the number one finance app in the App Store.

When you get it, use code LEXPODCAST.

Cash App lets you send money to friends,

buy Bitcoin, and invest in the stock market

with as little as $1.

It’s one of the best designed interfaces

of an app that I’ve ever used.

To me, good design is when everything is easy and natural.

Bad design is when the app gets in the way,

either because it’s buggy,

or because it tries too hard to be helpful.

I’m looking at you, Clippy, from Microsoft,

even though I love you.

Anyway, there’s a big part of my brain and heart

that loves to design things

and also to appreciate great design by others.

So again, if you get Cash App from the App Store

or Google Play and use the code LEXPODCAST,

you get $10, and Cash App will also donate $10 to FIRST,

an organization that is helping to advance

robotics and STEM education

for young people around the world.

And now, here’s my conversation with Richard Karp.

You wrote that at the age of 13,

you were first exposed to plane geometry

and was wonderstruck by the power and elegance

of form of proofs.

Are there problems, proofs, properties, ideas

in plane geometry that from that time

that you remember being mesmerized by

or just enjoying to go through to prove various aspects?

So Michael Rabin told me this story

about an experience he had when he was a young student

who was tossed out of his classroom for bad behavior

and was wandering through the corridors of his school

and came upon two older students

who were studying the problem of finding

the shortest distance between two nonoverlapping circles.

And Michael thought about it and said,

you take the straight line between the two centers

and the segment between the two circles is the shortest

because a straight line is the shortest distance

between the two centers.

And any other line connecting the circles

would be on a longer line.

And I thought, and he thought, and I agreed

that this was just elegance, the pure reasoning

could come up with such a result.

Certainly the shortest distance

from the two centers of the circles is a straight line.

Could you once again say what’s the next step in that proof?

Well, any segment joining the two circles,

if you extend it by taking the radius on each side,

you get a path with three edges

which connects the two centers.

And this has to be at least as long as the shortest path,

which is the straight line.

The straight line, yeah.

Wow, yeah, that’s quite simple.

So what is it about that elegance

that you just find compelling?

Well, just that you could establish a fact

about geometry beyond dispute by pure reasoning.

I also enjoy the challenge of solving puzzles

in plain geometry.

It was much more fun than the earlier mathematics courses

which were mostly about arithmetic operations

and manipulating them.

Was there something about geometry itself,

the slightly visual component of it?

Oh, yes, absolutely,

although I lacked three dimensional vision.

I wasn’t very good at three dimensional vision.

You mean being able to visualize three dimensional objects?

Three dimensional objects or surfaces,

hyperplanes and so on.

So there I didn’t have an intuition.

But for example, the fact that the sum of the angles

of a triangle is 180 degrees is proved convincingly.

And it comes as a surprise that that can be done.

Why is that surprising?

Well, it is a surprising idea, I suppose.

Why is that proved difficult?

It’s not, that’s the point.

It’s so easy and yet it’s so convincing.

Do you remember what is the proof that it adds up to 180?

You start at a corner and draw a line

parallel to the opposite side.

And that line sort of trisects the angle

between the other two sides.

And you get a half plane which has to add up to 180 degrees.

It has to add up to 180 degrees and it consists

in the angles by the equality of alternate angles.

What’s it called?

You get a correspondence between the angles

created along the side of the triangle

and the three angles of the triangle.

Has geometry had an impact on when you look into the future

of your work with combinatorial algorithms?

Has it had some kind of impact in terms of, yeah,

being able, the puzzles, the visual aspects

that were first so compelling to you?

Not Euclidean geometry particularly.

I think I use tools like linear programming

and integer programming a lot.

But those require high dimensional visualization

and so I tend to go by the algebraic properties.

Right, you go by the linear algebra

and not by the visualization.

Well, the interpretation in terms of, for example,

finding the highest point on a polyhedron

as in linear programming is motivating.

But again, I don’t have the high dimensional intuition

that would particularly inform me

so I sort of lean on the algebra.

So to linger on that point,

what kind of visualization do you do

when you’re trying to think about,

we’ll get to combinatorial algorithms,

but just algorithms in general.


What’s inside your mind

when you’re thinking about designing algorithms?

Or even just tackling any mathematical problem?

Well, I think that usually an algorithm

involves a repetition of some inner loop

and so I can sort of visualize the distance

from the desired solution as iteratively reducing

until you finally hit the exact solution.

And try to take steps that get you closer to the.

Try to take steps that get closer

and having the certainty of converging.

So it’s basically the mechanics of the algorithm

is often very simple,

but especially when you’re trying something out

on the computer.

So for example, I did some work

on the traveling salesman problem

and I could see there was a particular function

that had to be minimized

and it was fascinating to see the successive approaches

to the minimum, to the optimum.

You mean, so first of all,

traveling salesman problem is where you have to visit

every city without ever, the only ones.

Yeah, that’s right.

Find the shortest path through a set of cities.

Yeah, which is sort of a canonical standard,

a really nice problem that’s really hard.

Right, exactly, yes.

So can you say again what was nice

about being able to think about the objective function there

and maximizing it or minimizing it?

Well, just that as the algorithm proceeded,

you were making progress, continual progress,

and eventually getting to the optimum point.

So there’s two parts, maybe.

Maybe you can correct me.

First is like getting an intuition

about what the solution would look like

and or even maybe coming up with a solution

and two is proving that this thing

is actually going to be pretty good.

What part is harder for you?

Where’s the magic happen?

Is it in the first sets of intuitions

or is it in the messy details of actually showing

that it is going to get to the exact solution

and it’s gonna run at a certain complexity?

Well, the magic is just the fact

that the gap from the optimum decreases monotonically

and you can see it happening

and various metrics of what’s going on

are improving all along until finally you hit the optimum.

Perhaps later we’ll talk about the assignment problem

and I can illustrate.

It illustrates a little better.

Now zooming out again, as you write,

Don Knuth has called attention to a breed of people

who derive great aesthetic pleasure

from contemplating the structure of computational processes.

So Don calls these folks geeks

and you write that you remember the moment

you realized you were such a person,

you were shown the Hungarian algorithm

to solve the assignment problem.

So perhaps you can explain what the assignment problem is

and what the Hungarian algorithm is.

So in the assignment problem,

you have n boys and n girls

and you are given the desirability of,

or the cost of matching the ith boy

with the jth girl for all i and j.

You’re given a matrix of numbers

and you want to find the one to one matching

of the boys with the girls

such that the sum of the associated costs will be minimized.

So the best way to match the boys with the girls

or men with jobs or any two sets.

Any possible matching is possible or?

Yeah, all one to one correspondences are permissible.

If there is a connection that is not allowed,

then you can think of it as having an infinite cost.

I see, yeah.

So what you do is to depend on the observation

that the identity of the optimal assignment

or as we call it, the optimal permutation

is not changed if you subtract a constant

from any row or column of the matrix.

You can see that the comparison

between the different assignments is not changed by that.

Because if you decrease a particular row,

all the elements of a row by some constant,

all solutions decrease by an amount equal to that constant.

So the idea of the algorithm is to start with a matrix

of non negative numbers and keep subtracting

from rows or from columns.

Subtracting from rows or entire columns

in such a way that you subtract the same constant

from all the elements of that row or column

while maintaining the property

that all the elements are non negative.


Yeah, and so what you have to do

is find small moves which will decrease the total cost

while subtracting constants from rows or columns.

And there’s a particular way of doing that

by computing the kind of shortest path

through the elements in the matrix.

And you just keep going in this way

until you finally get a full permutation of zeros

while the matrix is non negative

and then you know that that has to be the cheapest.

Is that as simple as it sounds?

So the shortest path of the matrix part.

Yeah, the simplicity lies in how you find,

I oversimplified slightly what you,

you will end up subtracting a constant

from some rows or columns

and adding the same constant back to other rows and columns.

So as not to reduce any of the zero elements,

you leave them unchanged.

But each individual step modifies several rows and columns

by the same amount but overall decreases the cost.

So there’s something about that elegance

that made you go aha, this is a beautiful,

like it’s amazing that something like this,

something so simple can solve a problem like this.

Yeah, it’s really cool.

If I had mechanical ability,

I would probably like to do woodworking

or other activities where you sort of shape something

into something beautiful and orderly

and there’s something about the orderly systematic nature

of that iterative algorithm that is pleasing to me.

So what do you think about this idea of geeks

as Don Knuth calls them?

What do you think, is it something specific to a mindset

that allows you to discover the elegance

in computational processes or is this all of us,

can all of us discover this beauty?

Were you born this way?

I think so.

I always like to play with numbers.

I used to amuse myself by multiplying

by multiplying four digit decimal numbers in my head

and putting myself to sleep by starting with one

and doubling the number as long as I could go

and testing my memory, my ability to retain the information.

And I also read somewhere that you wrote

that you enjoyed showing off to your friends

by I believe multiplying four digit numbers.


Four digit numbers.

Yeah, I had a summer job at a beach resort

outside of Boston and the other employee,

I was the barker at a skee ball game.


I used to sit at a microphone saying come one,

come all, come in and play skee ball,

five cents to play, a nickel to win and so on.

That’s what a barker, I wasn’t sure if I should know

but barker, that’s, so you’re the charming,

outgoing person that’s getting people to come in.

Yeah, well I wasn’t particularly charming

but I could be very repetitious and loud.

And the other employees were sort of juvenile delinquents

who had no academic bent but somehow I found

that I could impress them by performing

this mental arithmetic.

Yeah, there’s something to that.

Some of the most popular videos on the internet

is there’s a YouTube channel called Numberphile

that shows off different mathematical ideas.

I see.

There’s still something really profoundly interesting

to people about math, the beauty of it.

Something, even if they don’t understand

the basic concept even being discussed,

there’s something compelling to it.

What do you think that is?

Any lessons you drew from your early teen years

when you were showing off to your friends with the numbers?

Like what is it that attracts us

to the beauty of mathematics do you think?

The general population, not just the computer scientists

and mathematicians.

I think that you can do amazing things.

You can test whether large numbers are prime.

You can solve little puzzles

about cannibals and missionaries.

And that’s a kind of achievement, it’s puzzle solving.

And at a higher level, the fact that you can do

this reasoning that you can prove

in an absolutely ironclad way that some of the angles

of a triangle is 180 degrees.

Yeah, it’s a nice escape from the messiness

of the real world where nothing can be proved.

So, and we’ll talk about it, but sometimes the ability

to map the real world into such problems

where you can’t prove it is a powerful step.


It’s amazing that we can do it.

Of course, another attribute of geeks

is they’re not necessarily endowed

with emotional intelligence, so they can live

in a world of abstractions without having

to master the complexities of dealing with people.

So just to link on the historical note,

as a PhD student in 1955, you joined the computational lab

at Harvard where Howard Aiken had built the Mark I

and the Mark IV computers.

Just to take a step back into that history,

what were those computers like?

The Mark IV filled a large room,

much bigger than this large office

that we were talking in now.

And you could walk around inside it.

There were rows of relays.

You could just walk around the interior

and the machine would sometimes fail because of bugs,

which literally meant flying creatures

landing on the switches.

So I never used that machine for any practical purpose.

The lab eventually acquired one of the earlier

commercial computers.

And this was already in the 60s?

No, in the mid 50s, or late 50s.

There was already commercial computers in the…

Yeah, we had a Univac, a Univac with 2,000 words of storage.

And so you had to work hard to allocate the memory properly

to also the excess time from one word to another

depended on the number of the particular words.

And so there was an art to sort of arranging

the storage allocation to make fetching data rapid.

Were you attracted to this actual physical world

implementation of mathematics?

So it’s a mathematical machine that’s actually doing

the math physically?

No, not at all.

I think I was attracted to the underlying algorithms.

But did you draw any inspiration?

So could you have imagined, like what did you imagine

was the future of these giant computers?

Could you have imagined that 60 years later

we’d have billions of these computers all over the world?

I couldn’t imagine that, but there was a sense

in the laboratory that this was the wave of the future.

In fact, my mother influenced me.

She told me that data processing was gonna be really big

and I should get into it.

You’re a smart woman.

Yeah, she was a smart woman.

And there was just a feeling that this was going

to change the world, but I didn’t think of it

in terms of personal computing.

I had no anticipation that we would be walking around

with computers in our pockets or anything like that.

Did you see computers as tools, as mathematical mechanisms

to analyze sort of the theoretical computer science,

or as the AI folks, which is an entire other community

of dreamers, as something that could one day

have human level intelligence?

Well, AI wasn’t very much on my radar.

I did read Turing’s paper about the…

The Turing Test, Computing and Intelligence.

Yeah, the Turing test.

What’d you think about that paper?

Was that just like science fiction?

I thought that it wasn’t a very good test

because it was too subjective.

So I didn’t feel that the Turing test

was really the right way to calibrate

how intelligent an algorithm could be.

But to linger on that, do you think it’s,

because you’ve come up with some incredible tests later on,

tests on algorithms, right, that are like strong,

reliable, robust across a bunch of different classes

of algorithms, but returning to this emotional mess

that is intelligence, do you think it’s possible

to come up with a test that’s as ironclad

as some of the computational complexity work?

Well, I think the greater question

is whether it’s possible to achieve human level intelligence.

Right, so first of all, let me, at the philosophical level,

do you think it’s possible to create algorithms

that reason and would seem to us

to have the same kind of intelligence as human beings?

It’s an open question.

It seems to me that most of the achievements

have operate within a very limited set of ground rules

and for a very limited, precise task,

which is a quite different situation

from the processes that go on in the minds of humans,

which where they have to sort of function

in changing environments, they have emotions,

they have physical attributes for exploring

their environment, they have intuition,

they have desires, emotions, and I don’t see anything

in the current achievements of what’s called AI

that come close to that capability.

I don’t think there’s any computer program

which surpasses a six month old child

in terms of comprehension of the world.

Do you think this complexity of human intelligence,

all the cognitive abilities we have,

all the emotion, do you think that could be reduced one day

or just fundamentally can it be reduced

to a set of algorithms or an algorithm?

So can a Turing machine achieve human level intelligence?

I am doubtful about that.

I guess the argument in favor of it

is that the human brain seems to achieve what we call

intelligence cognitive abilities of different kinds.

And if you buy the premise that the human brain

is just an enormous interconnected set of switches,

so to speak, then in principle, you should be able

to diagnose what that interconnection structure is like,

characterize the individual switches,

and build a simulation outside.

But while that may be true in principle,

that cannot be the way we’re eventually

gonna tackle this problem.

That does not seem like a feasible way to go about it.

So there is, however, an existence proof that

if you believe that the brain is just a network of neurons

operating by rules, I guess you could say

that that’s an existence proof of the capabilities

of a mechanism, but it would be almost impossible

to acquire the information unless we got enough insight

into the operation of the brain.

But there’s so much mystery there.

Do you think, what do you make of consciousness,

for example, as an example of something

we completely have no clue about?

The fact that we have this subjective experience.

Is it possible that this network of,

this circuit of switches is able to create

something like consciousness?

To know its own identity.

Yeah, to know the algorithm, to know itself.

To know itself.

I think if you try to define that rigorously,

you’d have a lot of trouble.

Yeah, that seems to be.

So I know that there are many who believe

that general intelligence can be achieved,

and there are even some who feel certain

that the singularity will come

and we will be surpassed by the machines

which will then learn more and more about themselves

and reduce humans to an inferior breed.

I am doubtful that this will ever be achieved.

Just for the fun of it, could you linger on why,

what’s your intuition, why you’re doubtful?

So there are quite a few people that are extremely worried

about this existential threat of artificial intelligence,

of us being left behind

by this super intelligent new species.

What’s your intuition why that’s not quite likely?

Just because none of the achievements in speech

or robotics or natural language processing

or creation of flexible computer assistants

or any of that comes anywhere near close

to that level of cognition.

What do you think about ideas of sort of,

if we look at Moore’s Law and exponential improvement

to allow us, that would surprise us?

Sort of our intuition fall apart

with exponential improvement because, I mean,

we’re not able to kind of,

we kind of think in linear improvement.

We’re not able to imagine a world

that goes from the Mark I computer to an iPhone X.


So do you think we could be really surprised

by the exponential growth?

Or on the flip side, is it possible

that also intelligence is actually way, way, way, way harder,

even with exponential improvement to be able to crack?

I don’t think any constant factor improvement

could change things.

I mean, given our current comprehension

of what cognition requires,

it seems to me that multiplying the speed of the switches

by a factor of a thousand or a million

will not be useful until we really understand

the organizational principle behind the network of switches.

Well, let’s jump into the network of switches

and talk about combinatorial algorithms if we could.

Let’s step back with the very basics.

What are combinatorial algorithms?

And what are some major examples

of problems they aim to solve?

A combinatorial algorithm is one which deals

with a system of discrete objects

that can occupy various states

or take on various values from a discrete set of values

and need to be arranged or selected

in such a way as to achieve some,

to minimize some cost function.

Or to prove the existence

of some combinatorial configuration.

So an example would be coloring the vertices of a graph.

What’s a graph?

Let’s step back.

So it’s fun to ask one of the greatest

computer scientists of all time

the most basic questions in the beginning of most books.

But for people who might not know,

but in general how you think about it, what is a graph?

A graph, that’s simple.

It’s a set of points, certain pairs of which

are joined by lines called edges.

And they sort of represent the,

in different applications represent the interconnections

between discrete objects.

So they could be the interactions,

interconnections between switches in a digital circuit

or interconnections indicating the communication patterns

of a human community.

And they could be directed or undirected

and then as you’ve mentioned before, might have costs.

Right, they can be directed or undirected.

They can be, you can think of them as,

if you think, if a graph were representing

a communication network, then the edge could be undirected

meaning that information could flow along it

in both directions or it could be directed

with only one way communication.

A road system is another example of a graph

with weights on the edges.

And then a lot of problems of optimizing the efficiency

of such networks or learning about the performance

of such networks are the object of combinatorial algorithms.

So it could be scheduling classes at a school

where the vertices, the nodes of the network

are the individual classes and the edges indicate

the constraints which say that certain classes

cannot take place at the same time

or certain teachers are available only for certain classes,

et cetera.

Or I talked earlier about the assignment problem

of matching the boys with the girls

where you have there a graph with an edge

from each boy to each girl with a weight

indicating the cost.

Or in logical design of computers,

you might want to find a set of so called gates,

switches that perform logical functions

which can be interconnected to each other

and perform logical functions which can be interconnected

to realize some function.

So you might ask how many gates do you need

in order for a circuit to give a yes output

if at least a given number of its inputs are ones

and no if fewer are present.

My favorite’s probably all the work with network flow.

So anytime you have, I don’t know why it’s so compelling

but there’s something just beautiful about it.

It seems like there’s so many applications

and communication networks and traffic flow

that you can map into these and then you could think

of pipes and water going through pipes

and you could optimize it in different ways.

There’s something always visually and intellectually

compelling to me about it.

And of course you’ve done work there.

Yeah, so there the edges represent channels

along which some commodity can flow.

It might be gas, it might be water, it might be information.

Maybe supply chain as well like products being.

Products flowing from one operation to another.

And the edges have a capacity which is the rate

at which the commodity can flow.

And a central problem is to determine

given a network of these channels.

In this case the edges are communication channels.

The challenge is to find the maximum rate

at which the information can flow along these channels

to get from a source to a destination.

And that’s a fundamental combinatorial problem

that I’ve worked on jointly with the scientist Jack Edmonds.

I think we’re the first to give a formal proof

that this maximum flow problem through a network

can be solved in polynomial time.

Which I remember the first time I learned that.

Just learning that in maybe even grad school.

I don’t think it was even undergrad.

No, algorithm, yeah.

Do network flows get taught in basic algorithms courses?

Yes, probably.

Okay, so yeah, I remember being very surprised

that max flow is a polynomial time algorithm.

That there’s a nice fast algorithm that solves max flow.

So there is an algorithm named after you in Edmonds.

The Edmond Carp algorithm for max flow.

So what was it like tackling that problem

and trying to arrive at a polynomial time solution?

And maybe you can describe the algorithm.

Maybe you can describe what’s the running time complexity

that you showed.

Yeah, well, first of all,

what is a polynomial time algorithm?

Perhaps we could discuss that.

So yeah, let’s actually just even, yeah.

What is algorithmic complexity?

What are the major classes of algorithm complexity?

So in a problem like the assignment problem

or scheduling schools or any of these applications,

you have a set of input data which might, for example,

be a set of vertices connected by edges

with you’re given for each edge the capacity of the edge.

And you have algorithms which are,

think of them as computer programs with operations

such as addition, subtraction, multiplication, division,

comparison of numbers, and so on.

And you’re trying to construct an algorithm

based on those operations, which will determine

in a minimum number of computational steps

the answer to the problem.

In this case, the computational step

is one of those operations.

And the answer to the problem is let’s say

the configuration of the network

that carries the maximum amount of flow.

And an algorithm is said to run in polynomial time

if as a function of the size of the input,

the number of vertices, the number of edges, and so on,

the number of basic computational steps grows

only as some fixed power of that size.

A linear algorithm would execute a number of steps

linearly proportional to the size.

Quadratic algorithm would be steps proportional

to the square of the size, and so on.

And algorithms whose running time is bounded

by some fixed power of the size

are called polynomial algorithms.


And that’s supposed to be relatively fast class

of algorithms.

That’s right.

Theoreticians take that to be the definition

of an algorithm being efficient.

And we’re interested in which problems can be solved

by such efficient algorithms.

One can argue whether that’s the right definition

of efficient because you could have an algorithm

whose running time is the 10,000th power

of the size of the input,

and that wouldn’t be really efficient.

And in practice, it’s oftentimes reducing

from an N squared algorithm to an N log N

or a linear time is practically the jump

that you wanna make to allow a real world system

to solve a problem.

Yeah, that’s also true because especially

as we get very large networks,

the size can be in the millions,

and then anything above N log N

where N is the size would be too much

for a practical solution.

Okay, so that’s polynomial time algorithms.

What other classes of algorithms are there?

What’s, so that usually they designate polynomials

of the letter P.


There’s also NP, NP complete and NP hard.


So can you try to disentangle those

by trying to define them simply?

Right, so a polynomial time algorithm

is one whose running time is bounded

by a polynomial in the size of the input.

Then the class of such algorithms is called P.

In the worst case, by the way, we should say, right?


So for every case of the problem.

Yes, that’s right, and that’s very important

that in this theory, when we measure the complexity

of an algorithm, we really measure the number of steps,

the growth of the number of steps in the worst case.

So you may have an algorithm that runs very rapidly

in most cases, but if there’s any case

where it gets into a very long computation,

that would increase the computational complexity

by this measure.

And that’s a very important issue

because there are, as we may discuss later,

there are some very important algorithms

which don’t have a good standing

from the point of view of their worst case performance

and yet are very effective.

So theoreticians are interested in P,

the class of problem solvable in polynomial time.

Then there’s NP, which is the class of problems

which may be hard to solve, but when confronted

with a solution, you can check it in polynomial time.

Let me give you an example there.

So if we look at the assignment problem,

so you have N boys, you have N girls,

the number of numbers that you need to write down

to specify the problem instance is N squared.

And the question is how many steps are needed to solve it?

And Jack Edmonds and I were the first to show

that it could be done in time and cubed.

Earlier algorithms required N to the fourth.

So as a polynomial function of the size of the input,

this is a fast algorithm.

Now to illustrate the class NP, the question is

how long would it take to verify

that a solution is optimal?

So for example, if the input was a graph,

we might want to find the largest clique in the graph

or a clique is a set of vertices such that any vertex,

each vertex in the set is adjacent to each of the others.

So the clique is a complete subgraph.

Yeah, so if it’s a Facebook social network,

everybody’s friends with everybody else, close clique.

No, that would be what’s called a complete graph.

It would be.

No, I mean within that clique.

Within that clique, yeah.

Yeah, they’re all friends.

So a complete graph is when?

Everybody is friendly.

As everybody is friends with everybody, yeah.

So the problem might be to determine whether

in a given graph there exists a clique of a certain size.

Now that turns out to be a very hard problem,

but if somebody hands you a clique and asks you to check

whether it is, hands you a set of vertices

and asks you to check whether it’s a clique,

you could do that simply by exhaustively looking

at all of the edges between the vertices and the clique

and verifying that they’re all there.

And that’s a polynomial time algorithm.

That’s a polynomial.

So the problem of finding the clique

appears to be extremely hard,

but the problem of verifying a clique

to see if it reaches a target number of vertices

is easy to verify.

So finding the clique is hard, checking it is easy.

Problems of that nature are called

nondeterministic polynomial time algorithms,

and that’s the class NP.

And what about NP complete and NP hard?

Okay, let’s talk about problems

where you’re getting a yes or no answer

rather than a numerical value.

So either there is a perfect matching

of the boys with the girls or there isn’t.

It’s clear that every problem in P is also in NP.

If you can solve the problem exactly,

then you can certainly verify the solution.

On the other hand, there are problems in the class NP.

This is the class of problems that are easy to check,

although they may be hard to solve.

It’s not at all clear that problems in NP lie in P.

So for example, if we’re looking

at scheduling classes at a school,

the fact that you can verify when handed a schedule

for the school, whether it meets all the requirements,

that doesn’t mean that you can find the schedule rapidly.

So intuitively, NP, nondeterministic polynomial checking

rather than finding, is going to be harder than,

is going to include, is easier.

Checking is easier, and therefore the class of problems

that can be checked appears to be much larger

than the class of problems that can be solved.

And then you keep adding appears to,

and sort of these additional words that designate

that we don’t know for sure yet.

We don’t know for sure.

So the theoretical question, which is considered

to be the most central problem

in theoretical computer science,

or at least computational complexity theory,

combinatorial algorithm theory,

the question is whether P is equal to NP.

If P were equal to NP, it would be amazing.

It would mean that every problem

where a solution can be rapidly checked

can actually be solved in polynomial time.

We don’t really believe that’s true.

If you’re scheduling classes at a school,

we expect that if somebody hands you a satisfying schedule,

you can verify that it works.

That doesn’t mean that you should be able

to find such a schedule.

So intuitively, NP encompasses a lot more problems than P.

So can we take a small tangent

and break apart that intuition?

So do you, first of all, think that the biggest

sort of open problem in computer science,

maybe mathematics, is whether P equals NP?

Do you think P equals NP,

or do you think P is not equal to NP?

If you had to bet all your money on it.

I would bet that P is unequal to NP,

simply because there are problems

that have been around for centuries

and have been studied intensively in mathematics,

and even more so in the last 50 years

since the P versus NP was stated.

And no polynomial time algorithms have been found

for these easy to check problems.

So one example is a problem that goes back

to the mathematician Gauss,

who was interested in factoring large numbers.

So we know what a number is prime

if it cannot be written as the product

of two or more numbers unequal to one.

So if we can factor a number like 91, it’s seven times 13.

But if I give you 20 digit or 30 digit numbers,

you’re probably gonna be at a loss

to have any idea whether they can be factored.

So the problem of factoring very large numbers

does not appear to have an efficient solution.

But once you have found the factors,

expressed the number as a product of two smaller numbers,

you can quickly verify that they are factors of the number.

And your intuition is a lot of people finding,

a lot of brilliant people have tried to find algorithms

for this one particular problem.

There’s many others like it that are really well studied

and it would be great to find an efficient algorithm for.

Right, and in fact, we have some results

that I was instrumental in obtaining following up on work

by the mathematician Stephen Cook

to show that within the class NP of easy to check problems,

easy to check problems, there’s a huge number

that are equivalent in the sense that either all of them

or none of them lie in P.

And this happens only if P is equal to NP.

So if P is unequal to NP, we would also know

that virtually all the standard combinatorial problems,

virtually all the standard combinatorial problems,

if P is unequal to NP, none of them can be solved

in polynomial time.

Can you explain how that’s possible

to tie together so many problems in a nice bunch

that if one is proven to be efficient, then all are?

The first and most important stage of progress

was a result by Stephen Cook who showed that a certain problem

called the satisfiability problem of propositional logic

is as hard as any problem in the class P.

So the propositional logic problem is expressed

in terms of expressions involving the logical operations

and, or, and not operating on variables

that can be either true or false.

So an instance of the problem would be some formula

involving and, or, and not.

And the question would be whether there is an assignment

of truth values to the variables in the problem

that would make the formula true.

So for example, if I take the formula A or B

and A or not B and not A or B and not A or not B

and take the conjunction of all four

of those so called expressions,

you can determine that no assignment of truth values

to the variables A and B will allow that conjunction

of what are called clauses to be true.

So that’s an example of a formula in propositional logic

involving expressions based on the operations and, or, and not.

That’s an example of a problem which is not satisfiable.

There is no solution that satisfies

all of those constraints.

I mean that’s like one of the cleanest and fundamental

problems in computer science.

It’s like a nice statement of a really hard problem.

It’s a nice statement of a really hard problem

and what Cook showed is that every problem in NP

can be reexpressed as an instance

of the satisfiability problem.

So to do that, he used the observation

that a very simple abstract machine

called the Turing machine can be used

to describe any algorithm.

An algorithm for any realistic computer

can be translated into an equivalent algorithm

on one of these Turing machines

which are extremely simple.

So a Turing machine, there’s a tape and you can

Yeah, you have data on a tape

and you have basic instructions,

a finite list of instructions which say,

if you’re reading a particular symbol on the tape

and you’re in a particular state,

then you can move to a different state

and change the state of the number

or the element that you were looking at,

the cell of the tape that you were looking at.

And that was like a metaphor and a mathematical construct

that Turing put together

to represent all possible computation.

All possible computation.

Now, one of these so called Turing machines

is too simple to be useful in practice,

but for theoretical purposes,

we can depend on the fact that an algorithm

for any computer can be translated

into one that would run on a Turing machine.

And then using that fact,

he could sort of describe

any possible non deterministic polynomial time algorithm.

Any algorithm for a problem in NP

could be expressed as a sequence of moves

of the Turing machine described

in terms of reading a symbol on the tape

while you’re in a given state

and moving to a new state and leaving behind a new symbol.

And given that fact

that any non deterministic polynomial time algorithm

can be described by a list of such instructions,

you could translate the problem

into the language of the satisfiability problem.

Is that amazing to you, by the way,

if you take yourself back

when you were first thinking about the space of problems?

How amazing is that?

It’s astonishing.

When you look at Cook’s proof,

it’s not too difficult to sort of figure out

why this is so,

but the implications are staggering.

It tells us that this, of all the problems in NP,

all the problems where solutions are easy to check,

they can all be rewritten

in terms of the satisfiability problem.

Yeah, it’s adding so much more weight

to the P equals NP question

because all it takes is to show that one algorithm

in this class.

So the P versus NP can be re expressed

as simply asking whether the satisfiability problem

of propositional logic is solvable in polynomial time.

But there’s more.

I encountered Cook’s paper

when he published it in a conference in 1971.

Yeah, so when I saw Cook’s paper

and saw this reduction of each of the problems in NP

by a uniform method to the satisfiability problem

of propositional logic,

that meant that the satisfiability problem

was a universal combinatorial problem.

And it occurred to me through experience I had had

in trying to solve other combinatorial problems

that there were many other problems

which seemed to have that universal structure.

And so I began looking for reductions

from the satisfiability to other problems.

And one of the other problems

would be the so called integer programming problem

of determining whether there’s a solution

to a set of linear inequalities involving integer variables.

Just like linear programming,

but there’s a constraint that the variables

must remain integers.

In fact, must be the zero or one

could only take on those values.

And that makes the problem much harder.

Yes, that makes the problem much harder.

And it was not difficult to show

that the satisfiability problem can be restated

as an integer programming problem.

So can you pause on that?

Was that one of the first mappings that you tried to do?

And how hard is that mapping?

You said it wasn’t hard to show,

but that’s a big leap.

It is a big leap, yeah.

Well, let me give you another example.

Another problem in NP

is whether a graph contains a clique of a given size.

And now the question is,

can we reduce the propositional logic problem

to the problem of whether there’s a clique

of a certain size?

Well, if you look at the propositional logic problem,

it can be expressed as a number of clauses,

each of which is a,

of the form A or B or C,

where A is either one of the variables in the problem

or the negation of one of the variables.

And an instance of the propositional logic problem

can be rewritten using operations of Boolean logic,

can be rewritten as the conjunction of a set of clauses,

the AND of a set of ORs,

where each clause is a disjunction, an OR of variables

or negated variables.

So the question in the satisfiability problem

is whether those clauses can be simultaneously satisfied.

Now, to satisfy all those clauses,

you have to find one of the terms in each clause,

which is going to be true in your truth assignment,

but you can’t make the same variable both true and false.

So if you have the variable A in one clause

and you want to satisfy that clause by making A true,

you can’t also make the complement of A true

in some other clause.

And so the goal is to make every single clause true

if it’s possible to satisfy this,

and the way you make it true is at least…

One term in the clause must be true.

Got it.

So now we, to convert this problem

to something called the independent set problem,

where you’re just sort of asking for a set of vertices

in a graph such that no two of them are adjacent,

sort of the opposite of the clique problem.

So we’ve seen that we can now express that as

finding a set of terms, one in each clause,

without picking both the variable

and the negation of that variable,

because if the variable is assigned the truth value,

the negated variable has to have the opposite truth value.

And so we can construct a graph where the vertices

are the terms in all of the clauses,

and you have an edge between two terms

if an edge between two occurrences of terms,

either if they’re both in the same clause,

because you’re only picking one element from each clause,

and also an edge between them if they represent

opposite values of the same variable,

because you can’t make a variable both true and false.

And so you get a graph where you have

all of these occurrences of variables,

you have edges, which mean that you’re not allowed

to choose both ends of the edge,

either because they’re in the same clause

or they’re negations of one another.

All right, and that’s a, first of all, sort of to zoom out,

that’s a really powerful idea that you can take a graph

and connect it to a logic equation somehow,

and do that mapping for all possible formulations

of a particular problem on a graph.


I mean, that still is hard for me to believe.

Yeah, it’s hard for me to believe.

It’s hard for me to believe that that’s possible.

That they’re, like, what do you make of that,

that there’s such a union of,

there’s such a friendship among all these problems across

that somehow are akin to combinatorial algorithms,

that they’re all somehow related?

I know it can be proven, but what do you make of it,

that that’s true?

Well, that they just have the same expressive power.

You can take any one of them

and translate it into the terms of the other.

The fact that they have the same expressive power

also somehow means that they can be translatable.

Right, and what I did in the 1971 paper

was to take 21 fundamental problems,

the commonly occurring problems of packing,

covering, matching, and so forth,

lying in the class NP,

and show that the satisfiability problem

can be reexpressed as any of those,

that any of those have the same expressive power.

And that was like throwing down the gauntlet

of saying there’s probably many more problems like this.


Saying that, look, that they’re all the same.

They’re all the same, but not exactly.

They’re all the same in terms of whether they are

rich enough to express any of the others.

But that doesn’t mean that they have

the same computational complexity.

But what we can say is that either all of these problems

or none of them are solvable in polynomial time.

Yeah, so what is NP completeness

and NP hard as classes?

Oh, that’s just a small technicality.

So when we’re talking about decision problems,

that means that the answer is just yes or no.

There is a clique of size 15

or there’s not a clique of size 15.

On the other hand, an optimization problem

would be asking find the largest clique.

The answer would not be yes or no.

It would be 15.

So when you’re asking for the,

when you’re putting a valuation on the different solutions

and you’re asking for the one with the highest valuation,

that’s an optimization problem.

And there’s a very close affinity

between the two kinds of problems.

But the counterpart of being the hardest decision problem,

the hardest yes, no problem,

the counterpart of that is to minimize

or maximize an objective function.

And so a problem that’s hardest in the class

when viewed in terms of optimization,

those are called NP hard rather than NP complete.

And NP complete is for decision problems.

So if somebody shows that P equals NP,

what do you think that proof will look like

if you were to put on yourself,

if it’s possible to show that as a proof

or to demonstrate an algorithm?

All I can say is that it will involve concepts

that we do not now have and approaches that we don’t have.

Do you think those concepts are out there

in terms of inside complexity theory,

inside of computational analysis of algorithms?

Do you think there’s concepts

that are totally outside of the box

that we haven’t considered yet?

I think that if there is a proof that P is equal to NP

or that P is unequal to NP,

it’ll depend on concepts that are now outside the box.

Now, if that’s shown either way, P equals NP or P not,

well, actually P equals NP,

what impact, you kind of mentioned a little bit,

but can you linger on it?

What kind of impact would it have

on theoretical computer science

and perhaps software based systems in general?

Well, I think it would have enormous impact on the world

in either way case.

If P is unequal to NP, which is what we expect,

then we know that for the great majority

of the combinatorial problems that come up,

since they’re known to be NP complete,

we’re not going to be able to solve them

by efficient algorithms.

However, there’s a little bit of hope

in that it may be that we can solve most instances.

All we know is that if a problem is not NP,

then it can’t be solved efficiently on all instances.

But basically, if we find that P is unequal to NP,

it will mean that we can’t expect always

to get the optimal solutions to these problems.

And we have to depend on heuristics

that perhaps work most of the time

or give us good approximate solutions, but not.

So we would turn our eye towards the heuristics

with a little bit more acceptance and comfort on our hearts.


Okay, so let me ask a romanticized question.

What to you is one of the most

or the most beautiful combinatorial algorithm

in your own life or just in general in the field

that you’ve ever come across or have developed yourself?

Oh, I like the stable matching problem

or the stable marriage problem very much.

What’s the stable matching problem?


Imagine that you want to marry off N boys with N girls.

And each boy has an ordered list

of his preferences among the girls.

His first choice, his second choice,

through her, Nth choice.

And each girl also has an ordering of the boys,

his first choice, second choice, and so on.

And we’ll say that a matching,

a one to one matching of the boys with the girls is stable

if there are no two couples in the matching

such that the boy in the first couple

prefers the girl in the second couple to her mate

and she prefers the boy to her current mate.

In other words, if the matching is stable

if there is no pair who want to run away with each other

leaving their partners behind.

Gosh, yeah.


Actually, this is relevant to matching residents

with hospitals and some other real life problems,

although not quite in the form that I described.

So it turns out that there is,

for any set of preferences, a stable matching exists.

And moreover, it can be computed

by a simple algorithm

in which each boy starts making proposals to girls.

And if the girl receives the proposal,

she accepts it tentatively,

but she can drop it later

if she gets a better proposal from her point of view.

And the boys start going down their lists

proposing to their first, second, third choices

until stopping when a proposal is accepted.

But the girls meanwhile are watching the proposals

that are coming into them.

And the girl will drop her current partner

if she gets a better proposal.

And the boys never go back through the list?

They never go back, yeah.

So once they’ve been denied.

They don’t try again.

They don’t try again

because the girls are always improving their status

as they receive better and better proposals.

The boys are going down their lists starting

with their top preferences.

And one can prove that the process will come to an end

where everybody will get matched with somebody

and you won’t have any pair

that want to abscond from each other.

Do you find the proof or the algorithm itself beautiful?

Or is it the fact that with the simplicity

of just the two marching,

I mean the simplicity of the underlying rule

of the algorithm, is that the beautiful part?

Both I would say.

And you also have the observation that you might ask

who is better off, the boys who are doing the proposing

or the girls who are reacting to proposals.

And it turns out that it’s the boys

who are doing the best.

That is, each boy is doing at least as well

as he could do in any other staple matching.

So there’s a sort of lesson for the boys

that you should go out and be proactive

and make those proposals.

Go for broke.

I don’t know if this is directly mappable philosophically

to our society, but certainly seems

like a compelling notion.

And like you said, there’s probably a lot

of actual real world problems that this could be mapped to.

Yeah, well you get complications.

For example, what happens when a husband and wife

want to be assigned to the same hospital?

So you have to take those constraints into account.

And then the problem becomes NP hard.

Why is it a problem for the husband and wife

to be assigned to the same hospital?

No, it’s desirable.

Or at least go to the same city.

So you can’t, if you’re assigning residents to hospitals.

And then you have some preferences

for the husband and the wife or for the hospitals.

The residents have their own preferences.

Residents both male and female have their own preferences.

The hospitals have their preferences.

But if resident A, the boy, is going to Philadelphia,

then you’d like his wife also to be assigned

to a hospital in Philadelphia.

Which step makes it a NP hard problem that you mentioned?

The fact that you have this additional constraint.

That it’s not just the preferences of individuals,

but the fact that the two partners to a marriage

have to be assigned to the same place.

I’m being a little dense.

The perfect matching, no, not the perfect,

stable matching is what you referred to.

That’s when two partners are trying to.

Okay, what’s confusing you is that in the first

interpretation of the problem,

I had boys matching with girls.


In the second interpretation,

you have humans matching with institutions.

With institutions.

I, and there’s a coupling between within the,

gotcha, within the humans.


Any added little constraint will make it an NP hard problem.

Well, yeah.


By the way, the algorithm you mentioned

wasn’t one of yours or no?

No, no, that was due to Gale and Shapley

and my friend David Gale passed away

before he could get part of a Nobel Prize,

but his partner Shapley shared in a Nobel Prize

with somebody else for.


For economics.

For ideas stemming from the stable matching idea.

So you’ve also have developed yourself

some elegant, beautiful algorithms.

Again, picking your children,

so the Robin Karp algorithm for string searching,

pattern matching, Edmund Karp algorithm for max flows

we mentioned, Hopcroft Karp algorithm for finding

maximum cardinality matchings in bipartite graphs.

Is there ones that stand out to you,

ones you’re most proud of or just

whether it’s beauty, elegance,

or just being the right discovery development

in your life that you’re especially proud of?

I like the Rabin Karp algorithm

because it illustrates the power of randomization.

So the problem there

is to decide whether a given long string of symbols

from some alphabet contains a given word,

whether a particular word occurs

within some very much longer word.

And so the idea of the algorithm

is to associate with the word that we’re looking for,

a fingerprint, some number,

or some combinatorial object that describes that word,

and then to look for an occurrence of that same fingerprint

as you slide along the longer word.

And what we do is we associate with each word a number.

So first of all, we think of the letters

that occur in a word as the digits of, let’s say,

decimal or whatever base here,

whatever number of different symbols there are.

That’s the base of the numbers, yeah.

Right, so every word can then be thought of as a number

with the letters being the digits of that number.

And then we pick a random prime number in a certain range,

and we take that word viewed as a number,

and take the remainder on dividing that number by the prime.

So coming up with a nice hash function.

It’s a kind of hash function.

Yeah, it gives you a little shortcut

for that particular word.

Yeah, so that’s the…

It’s very different than other algorithms of its kind

that we’re trying to do search, string matching.

Yeah, which usually are combinatorial

and don’t involve the idea of taking a random fingerprint.


And doing the fingerprinting has two advantages.

One is that as we slide along the long word,

digit by digit, we keep a window of a certain size,

the size of the word we’re looking for,

and we compute the fingerprint

of every stretch of that length.

And it turns out that just a couple of arithmetic operations

will take you from the fingerprint of one part

to what you get when you slide over by one position.

So the computation of all the fingerprints is simple.

And secondly, it’s unlikely if the prime is chosen randomly

from a certain range that you will get two of the segments

in question having the same fingerprint.


And so there’s a small probability of error

which can be checked after the fact,

and also the ease of doing the computation

because you’re working with these fingerprints

which are remainder’s modulo some big prime.

So that’s the magical thing about randomized algorithms

is that if you add a little bit of randomness,

it somehow allows you to take a pretty naive approach,

a simple looking approach, and allow it to run extremely well.

So can you maybe take a step back and say

what is a randomized algorithm, this category of algorithms?

Well, it’s just the ability to draw a random number

from such, from some range

or to associate a random number with some object

or to draw that random from some set.

So another example is very simple

if we’re conducting a presidential election

and we would like to pick the winner.

In principle, we could draw a random sample

of all of the voters in the country.

And if it was of substantial size, say a few thousand,

then the most popular candidate in that group

would be very likely to be the correct choice

that would come out of counting all the millions of votes.

And of course we can’t do this because first of all,

everybody has to feel that his or her vote counted.

And secondly, we can’t really do a purely random sample

from that population.

And I guess thirdly, there could be a tie

in which case we wouldn’t have a significant difference

between two candidates.

But those things aside,

if you didn’t have all that messiness of human beings,

you could prove that that kind of random picking

would come up again.

You just said random picking would solve the problem

with a very low probability of error.

Another example is testing whether a number is prime.

So if I wanna test whether 17 is prime,

I could pick any number between one and 17,

raise it to the 16th power modulo 17,

and you should get back the original number.

That’s a famous formula due to Fermat about,

it’s called Fermat’s Little Theorem,

that if you take any number a in the range

zero through n minus one,

and raise it to the n minus 1th power modulo n,

you’ll get back the number a if a is prime.

So if you don’t get back the number a,

that’s a proof that a number is not prime.

And you can show that suitably defined

the probability that you will get a value unequaled,

you will get a violation of Fermat’s result is very high.

And so this gives you a way of rapidly proving

that a number is not prime.

It’s a little more complicated than that

because there are certain values of n

where something a little more elaborate has to be done,

but that’s the basic idea.

Taking an identity that holds for primes,

and therefore, if it ever fails on any instance

for a non prime, you know that the number is not prime.

It’s a quick choice, a fast choice,

fast proof that a number is not prime.

Can you maybe elaborate a little bit more

what’s your intuition why randomness works so well

and results in such simple algorithms?

Well, the example of conducting an election

where you could take, in theory, you could take a sample

and depend on the validity of the sample

to really represent the whole

is just the basic fact of statistics,

which gives a lot of opportunities.

And I actually exploited that sort of random sampling idea

in designing an algorithm

for counting the number of solutions

that satisfy a particular formula

and propositional logic.

A particular, so some version of the satisfiability problem?

A version of the satisfiability problem.

Is there some interesting insight

that you wanna elaborate on,

like what some aspect of that algorithm

that might be useful to describe?

So you have a collection of formulas

and you want to count the number of solutions

that satisfy at least one of the formulas.

And you can count the number of solutions

that satisfy any particular one of the formulas,

but you have to account for the fact

that that solution might be counted many times

if it solves more than one of the formulas.

And so what you do is you sample from the formulas

according to the number of solutions

that satisfy each individual one.

In that way, you draw a random solution,

but then you correct by looking at

the number of formulas that satisfy that random solution

and don’t double count.

So you can think of it this way.

So you have a matrix of zeros and ones

and you wanna know how many columns of that matrix

contain at least one one.

And you can count in each row how many ones there are.

So what you can do is draw from the rows

according to the number of ones.

If a row has more ones, it gets drawn more frequently.

But then if you draw from that row,

you have to go up the column

and looking at where that same one is repeated

in different rows and only count it as a success or a hit

if it’s the earliest row that contains the one.

And that gives you a robust statistical estimate

of the total number of columns

that contain at least one of the ones.

So that is an example of the same principle

that was used in studying random sampling.

Another viewpoint is that if you have a phenomenon

that occurs almost all the time,

then if you sample one of the occasions where it occurs,

you’re most likely to,

and you’re looking for an occurrence,

a random occurrence is likely to work.

So that comes up in solving identities,

solving algebraic identities.

You get two formulas that may look very different.

You wanna know if they’re really identical.

What you can do is just pick a random value

and evaluate the formulas at that value

and see if they agree.

And you depend on the fact

that if the formulas are distinct,

then they’re gonna disagree a lot.

And so therefore, a random choice

will exhibit the disagreement.

If there are many ways for the two to disagree

and you only need to find one disagreement,

then random choice is likely to yield it.

And in general, so we’ve just talked

about randomized algorithms,

but we can look at the probabilistic analysis of algorithms.

And that gives us an opportunity to step back

and as you said, everything we’ve been talking about

is worst case analysis.

Could you maybe comment on the usefulness

and the power of worst case analysis

versus best case analysis, average case, probabilistic?

How do we think about the future

of theoretical computer science, computer science

in the kind of analysis we do of algorithms?

Does worst case analysis still have a place,

an important place?

Or do we want to try to move forward

towards kind of average case analysis?

And what are the challenges there?

So if worst case analysis shows

that an algorithm is always good,

that’s fine.

If worst case analysis is used to show that the problem,

that the solution is not always good,

then you have to step back and do something else

to ask how often will you get a good solution?

Just to pause on that for a second,

that’s so beautifully put

because I think we tend to judge algorithms.

We throw them in the trash

the moment their worst case is shown to be bad.

Right, and that’s unfortunate.

I think a good example is going back

to the satisfiability problem.

There are very powerful programs called SAT solvers

which in practice fairly reliably solve instances

with many millions of variables

that arise in digital design

or in proving programs correct in other applications.

And so in many application areas,

even though satisfiability as we’ve already discussed

is NP complete, the SAT solvers will work so well

that the people in that discipline

tend to think of satisfiability as an easy problem.

So in other words, just for some reason

that we don’t entirely understand,

the instances that people formulate

in designing digital circuits or other applications

are such that satisfiability is not hard to check

and even searching for a satisfying solution

can be done efficiently in practice.

And there are many examples.

For example, we talked about the traveling salesman problem.

So just to refresh our memories,

the problem is you’ve got a set of cities,

you have pairwise distances between cities

and you want to find a tour through all the cities

that minimizes the total cost of all the edges traversed,

all the trips between cities.

The problem is NP hard,

but people using integer programming codes

together with some other mathematical tricks

can solve geometric instances of the problem

where the cities are, let’s say points in the plane

and get optimal solutions to problems

with tens of thousands of cities.

Actually, it’ll take a few computer months

to solve a problem of that size,

but for problems of size a thousand or two,

it’ll rapidly get optimal solutions,

provably optimal solutions,

even though again, we know that it’s unlikely

that the traveling salesman problem

can be solved in polynomial time.

Are there methodologies like rigorous systematic methodologies

for, you said in practice.

In practice, this algorithm’s pretty good.

Are there systematic ways of saying

in practice, this algorithm’s pretty good?

So in other words, average case analysis.

Or you’ve also mentioned that average case

kind of requires you to understand what the typical case is,

typical instances, and that might be really difficult.

That’s very difficult.

So after I did my original work

on showing all these problems through NP complete,

I looked around for a way to shed some positive light

on combinatorial algorithms.

And what I tried to do was to study problems,

behavior on the average or with high probability.

But I had to make some assumptions

about what’s the probability space?

What’s the sample space?

What do we mean by typical problems?

That’s very hard to say.

So I took the easy way out

and made some very simplistic assumptions.

So I assumed, for example,

that if we were generating a graph

with a certain number of vertices and edges,

then we would generate the graph

by simply choosing one edge at a time at random

until we got the right number of edges.

That’s a particular model of random graphs

that has been studied mathematically a lot.

And within that model,

I could prove all kinds of wonderful things,

I and others who also worked on this.

So we could show that we know exactly

how many edges there have to be

in order for there be a so called Hamiltonian circuit.

That’s a cycle that visits each vertex exactly once.

We know that if the number of edges

is a little bit more than n log n,

where n is the number of vertices,

then such a cycle is very likely to exist.

And we can give a heuristic

that will find it with high probability.

And the community in which I was working

got a lot of results along these lines.

But the field tended to be rather lukewarm

about accepting these results as meaningful

because we were making such a simplistic assumption

about the kinds of graphs that we would be dealing with.

So we could show all kinds of wonderful things,

it was a great playground, I enjoyed doing it.

But after a while, I concluded that

it didn’t have a lot of bite

in terms of the practical application.

Oh the, okay, so there’s too much

into the world of toy problems.


That can, okay.

But all right, is there a way to find

nice representative real world impactful instances

of a problem on which demonstrate that an algorithm is good?

So this is kind of like the machine learning world,

that’s kind of what they at his best tries to do

is find a data set from like the real world

and show the performance, all the conferences

are all focused on beating the performance

of on that real world data set.

Is there an equivalent in complexity analysis?

Not really, Don Knuth started to collect examples

of graphs coming from various places.

So he would have a whole zoo of different graphs

that he could choose from and he could study

the performance of algorithms on different types of graphs.

But there it’s really important and compelling

to be able to define a class of graphs.

The actual act of defining a class of graphs

that you’re interested in, it seems to be

a non trivial step if we’re talking about instances

that we should care about in the real world.

Yeah, there’s nothing available there

that would be analogous to the training set

for supervised learning where you sort of assume

that the world has given you a bunch

of examples to work with.

We don’t really have that for problems,

for combinatorial problems on graphs and networks.

You know, there’s been a huge growth,

a big growth of data sets available.

Do you think some aspect of theoretical computer science

might be contradicting my own question while saying it,

but will there be some aspect,

an empirical aspect of theoretical computer science

which will allow the fact that these data sets are huge,

we’ll start using them for analysis.

Sort of, you know, if you want to say something

about a graph algorithm, you might take

a social network like Facebook and looking at subgraphs

of that and prove something about the Facebook graph

and be respected, and at the same time,

be respected in the theoretical computer science community.

That hasn’t been achieved yet, I’m afraid.

Is that P equals NP, is that impossible?

Is it impossible to publish a successful paper

in the theoretical computer science community

that shows some performance on a real world data set?

Or is that really just those are two different worlds?

They haven’t really come together.

I would say that there is a field

of experimental algorithmics where people,

sometimes they’re given some family of examples.

Sometimes they just generate them at random

and they report on performance,

but there’s no convincing evidence

that the sample is representative of anything at all.

So let me ask, in terms of breakthroughs

and open problems, what are the most compelling

open problems to you and what possible breakthroughs

do you see in the near term

in terms of theoretical computer science?

Well, there are all kinds of relationships

among complexity classes that can be studied,

just to mention one thing, I wrote a paper

with Richard Lipton in 1979,

where we asked the following question.

If you take a combinatorial problem in NP, let’s say,

and you choose, and you pick the size of the problem,

say it’s a traveling salesman problem, but of size 52,

and you ask, could you get an efficient,

a small Boolean circuit tailored for that size, 52,

where you could feed the edges of the graph

in as Boolean inputs and get, as an output,

the question of whether or not

there’s a tour of a certain length.

And that would, in other words, briefly,

what you would say in that case

is that the problem has small circuits,

polynomial size circuits.

Now, we know that if P is equal to NP,

then, in fact, these problems will have small circuits,

but what about the converse?

Could a problem have small circuits,

meaning that an algorithm tailored to any particular size

could work well, and yet not be a polynomial time algorithm?

That is, you couldn’t write it as a single,

uniform algorithm, good for all sizes.

Just to clarify, small circuits

for a problem of particular size,

by small circuits for a problem of particular size,

or even further constraint,

small circuit for a particular…

No, for all the inputs of that size.

Is that a trivial problem for a particular instance?

So, coming up, an automated way

of coming up with a circuit.

I guess that’s just an answer.

That would be hard, yeah.

But there’s the existential question.

Everybody talks nowadays about existential questions.

Existential challenges.

You could ask the question,

does the Hamiltonian circuit problem

have a small circuit for every size,

for each size, a different small circuit?

In other words, could you tailor solutions

depending on the size, and get polynomial size?

Even if P is not equal to NP.


That would be fascinating if that’s true.

Yeah, what we proved is that if that were possible,

then something strange would happen in complexity theory.

Some high level class which I could briefly describe,

something strange would happen.

So, I’ll take a stab at describing what I mean.

Sure, let’s go there.

So, we have to define this hierarchy

in which the first level of the hierarchy is P,

and the second level is NP.

And what is NP?

NP involves statements of the form

there exists a something such that something holds.

So, for example, there exists the coloring

such that a graph can be colored

with only that number of colors.

Or there exists a Hamiltonian circuit.

There’s a statement about this graph.

Yeah, so the NP deals with statements of that kind,

that there exists a solution.

Now, you could imagine a more complicated expression

which says for all x there exists a y

such that some proposition holds involving both x and y.

So, that would say, for example, in game theory,

for all strategies for the first player,

there exists a strategy for the second player

such that the first player wins.

That would be at the second level of the hierarchy.

The third level would be there exists an A

such that for all B there exists a C,

that something holds.

And you could imagine going higher and higher

in the hierarchy.

And you’d expect that the complexity classes

that correspond to those different cases

would get bigger and bigger.

What do you mean by bigger and bigger?

Sorry, sorry.

They’d get harder and harder to solve.

Harder and harder, right.

Harder and harder to solve.

And what Lipton and I showed was

that if NP had small circuits,

then this hierarchy would collapse down

to the second level.

In other words, you wouldn’t get any more mileage

by complicating your expressions with three quantifiers

or four quantifiers or any number.

I’m not sure what to make of that exactly.

Well, I think it would be evidence

that NP doesn’t have small circuits

because something so bizarre would happen.

But again, it’s only evidence, not proof.

Well, yeah, that’s not even evidence

because you’re saying P is not equal to NP

because something bizarre has to happen.

I mean, that’s proof by the lack of bizarreness

in our science.

But it seems like just the very notion

of P equals NP would be bizarre.

So any way you arrive at, there’s no way.

You have to fight the dragon at some point.

Yeah, okay.

Well, anyway, for whatever it’s worth,

that’s what we proved.


So that’s a potential space of interesting problems.


Let me ask you about this other world

that of machine learning, of deep learning.

What’s your thoughts on the history

and the current progress of machine learning field

that’s often progressed sort of separately

as a space of ideas and space of people

than the theoretical computer science

or just even computer science world?

Yeah, it’s really very different

from the theoretical computer science world

because the results about it,

algorithmic performance tend to be empirical.

It’s more akin to the world of SAT solvers

where we observe that for formulas arising in practice,

the solver does well.

So it’s of that type.

We’re moving into the empirical evaluation of algorithms.

Now, it’s clear that there’ve been huge successes

in image processing, robotics,

natural language processing, a little less so,

but across the spectrum of game playing is another one.

There’ve been great successes and one of those effects

is that it’s not too hard to become a millionaire

if you can get a reputation in machine learning

and there’ll be all kinds of companies

that will be willing to offer you the moon

because they think that if they have AI at their disposal,

then they can solve all kinds of problems.

But there are limitations.

One is that the solutions that you get

to supervise learning problems

through convolutional neural networks

seem to perform amazingly well

even for inputs that are outside the training set.

But we don’t have any theoretical understanding

of why that’s true.

Secondly, the solutions, the networks that you get

are very hard to understand

and so very little insight comes out.

So yeah, yeah, they may seem to work on your training set

and you may be able to discover whether your photos occur

in a different sample of inputs or not,

but we don’t really know what’s going on.

We don’t know the features that distinguish the photographs

or the objects are not easy to characterize.

Well, it’s interesting because you mentioned

coming up with a small circuit to solve

a particular size problem.

It seems that neural networks are kind of small circuits.

In a way, yeah.

But they’re not programs.

Sort of like the things you’ve designed

are algorithms, programs, algorithms.

Neural networks aren’t able to develop algorithms

to solve a problem.

Well, they are algorithms.

It’s just that they’re…

But sort of, yeah, it could be a semantic question,

but there’s not a algorithmic style manipulation

of the input.

Perhaps you could argue there is.

Yeah, well.

It feels a lot more like a function of the input.

Yeah, it’s a function.

It’s a computable function.

Once you have the network,

you can simulate it on a given input

and figure out the output.

But if you’re trying to recognize images,

then you don’t know what features of the image

are really being determinant of what the circuit is doing.

The circuit is sort of very intricate

and it’s not clear that the simple characteristics

that you’re looking for,

the edges of the objects or whatever they may be,

they’re not emerging from the structure of the circuit.

Well, it’s not clear to us humans,

but it’s clear to the circuit.

Yeah, well, right.

I mean, it’s not clear to sort of the elephant

how the human brain works,

but it’s clear to us humans,

we can explain to each other our reasoning

and that’s why the cognitive science

and psychology field exists.

Maybe the whole thing of being explainable to humans

is a little bit overrated.

Oh, maybe, yeah.

I guess you can say the same thing about our brain

that when we perform acts of cognition,

we have no idea how we do it really.

We do though, I mean, at least for the visual system,

the auditory system and so on,

we do get some understanding of the principles

that they operate under,

but for many deeper cognitive tasks, we don’t have that.

That’s right.

Let me ask, you’ve also been doing work on bioinformatics.

Does it amaze you that the fundamental building blocks?

So if we take a step back and look at us humans,

the building blocks used by evolution

to build us intelligent human beings

is all contained there in our DNA.

It’s amazing and what’s really amazing

is that we are beginning to learn how to edit DNA,

which is very, very, very fascinating.

This ability to take a sequence,

find it in the genome and do something to it.

I mean, that’s really taking our biological systems

towards the world of algorithms.

Yeah, but it raises a lot of questions.

You have to distinguish between doing it on an individual

or doing it on somebody’s germline,

which means that all of their descendants will be affected.

So that’s like an ethical.

Yeah, so it raises very severe ethical questions.

And even doing it on individuals,

there’s a lot of hubris involved

that you can assume that knocking out a particular gene

is gonna be beneficial

because you don’t know what the side effects

are going to be.

So we have this wonderful new world of gene editing,

which is very, very impressive

and it could be used in agriculture,

it could be used in medicine in various ways.

But very serious ethical problems arise.

What are to you the most interesting places

where algorithms, sort of the ethical side

is an exceptionally challenging thing

that I think we’re going to have to tackle

with all of genetic engineering.

But on the algorithmic side,

there’s a lot of benefit that’s possible.

So is there areas where you see exciting possibilities

for algorithms to help model, optimize,

study biological systems?

Yeah, I mean, we can certainly analyze genomic data

to figure out which genes are operative in the cell

and under what conditions

and which proteins affect one another,

which proteins physically interact.

We can sequence proteins and modify them.

Is there some aspect of that

that’s a computer science problem

or is that still fundamentally a biology problem?

Well, it’s a big data,

it’s a statistical big data problem for sure.

So the biological data sets are increasing,

our ability to study our ancestry,

to study the tendencies towards disease,

to personalize treatment according to what’s in our genomes

and what tendencies for disease we have,

to be able to predict what troubles might come upon us

in the future and anticipate them,

to understand whether you,

for a woman, whether her proclivity for breast cancer

is so strong enough that she would want

to take action to avoid it.

You dedicate your 1985 Turing Award lecture

to the memory of your father.

What’s your fondest memory of your dad?

Seeing him standing in front of a class at the blackboard,

drawing perfect circles by hand

and showing his ability to attract the interest

of the motley collection of eighth grade students

that he was teaching.

When did you get a chance to see him

draw the perfect circles?

On rare occasions, I would get a chance

to sneak into his classroom and observe him.

And I think he was at his best in the classroom.

I think he really came to life and had fun,

not only teaching, but engaging in chit chat

with the students and ingratiating himself

with the students.

And what I inherited from that is the great desire

to be a teacher.

I retired recently and a lot of my former students came,

students with whom I had done research

or who had read my papers or who had been in my classes.

And when they talked about me,

they talked not about my 1979 paper or 1992 paper,

but about what came away in my classes.

And not just the details, but just the approach

and the manner of teaching.

And so I sort of take pride in the,

at least in my early years as a faculty member at Berkeley,

I was exemplary in preparing my lectures

and I always came in prepared to the teeth,

and able therefore to deviate according

to what happened in the class,

and to really provide a model for the students.

So is there advice you can give out for others

on how to be a good teacher?

So preparation is one thing you’ve mentioned,

being exceptionally well prepared,

but there are other things,

pieces of advice that you can impart?

Well, the top three would be preparation, preparation,

and preparation.

Why is preparation so important, I guess?

It’s because it gives you the ease

to deal with any situation that comes up in the classroom.

And if you discover that you’re not getting through one way,

you can do it another way.

If the students have questions,

you can handle the questions.

Ultimately, you’re also feeling the crowd,

the students of what they’re struggling with,

what they’re picking up,

just looking at them through the questions,

but even just through their eyes.

Yeah, that’s right.

And because of the preparation, you can dance.

You can dance, you can say it another way,

or give it another angle.

Are there, in particular, ideas and algorithms

of computer science that you find

were big aha moments for students,

where they, for some reason, once they got it,

it clicked for them and they fell in love

with computer science?

Or is it individual, is it different for everybody?

It’s different for everybody.

You have to work differently with students.

Some of them just don’t need much influence.

They’re just running with what they’re doing

and they just need an ear now and then.

Others need a little prodding.

Others need to be persuaded to collaborate among themselves

rather than working alone.

They have their personal ups and downs,

so you have to deal with each student as a human being

and bring out the best.

Humans are complicated.


Perhaps a silly question.

If you could relive a moment in your life outside of family

because it made you truly happy,

or perhaps because it changed the direction of your life

in a profound way, what moment would you pick?

I was kind of a lazy student as an undergraduate,

and even in my first year in graduate school.

And I think it was when I started doing research,

I had a couple of summer jobs where I was able to contribute

and I had an idea.

And then there was one particular course

on mathematical methods and operations research

where I just gobbled up the material

and I scored 20 points higher than anybody else in the class

then came to the attention of the faculty.

And it made me realize that I had some ability

that I was going somewhere.

You realize you’re pretty good at this thing.

I don’t think there’s a better way to end it, Richard.

It was a huge honor.

Thank you for decades of incredible work.

Thank you for talking to me.

Thank you, it’s been a great pleasure.

You’re a superb interviewer.

I’ll stop it.

Thanks for listening to this conversation with Richard Karp.

And thank you to our sponsors, 8sleep and Cash App.

Please consider supporting this podcast

by going to 8sleep.com slash Lex

to check out their awesome mattress

and downloading Cash App and using code LexPodcast.

Click the links, buy the stuff,

even just visiting the site

but also considering the purchase.

Helps them know that this podcast

is worth supporting in the future.

It really is the best way to support this journey I’m on.

If you enjoy this thing, subscribe on YouTube,

review it with Five Stars and Apple Podcast,

support it on Patreon, connect with me on Twitter

at Lex Friedman if you can figure out how to spell that.

And now let me leave you with some words from Isaac Asimov.

I do not fear computers.

I fear lack of them.

Thank you for listening and hope to see you next time.

comments powered by Disqus