Highlights of FOCS Theory Day



A summary of a great day of talks before FOCS at Theory Day



Dick Karp was the leadoff speaker this Saturday at the FOCS Theory Day

in Atlanta. He was followed by Mihalis Yannakakis, Noga Alon, and Manny Blum. Sounds to me like a lineup for a baseball team that is in the World Series. I can almost hear an announcer saying:

Now batting cleanup, Mannnnny Blummmmm.

And the crowd goes wild.

Today I thought I would write a summary of what Dick and the others said Saturday. The talks were webcast, but perhaps not all of you watched them. In any event I will add some additional comments that I hope you enjoy.



All of these speakers always give great talks, and this day was no exception. I did notice that Karp and Blum both have titles that ask a question. Is this meaningful?

I cannot resist one story about Dick. Years ago he visited Atlanta to give the keynote at a conference. Rich DeMillo was the program chair, and he and I went out to the airport to pick Karp up. We planned to eat lunch, and then get Dick to the venue for his address. During the lunch we had a wonderful time chatting with Karp. Finally, at one point Dick said that he had a small issue that he was worried about. We of course asked what it was—we were prepared to help him in anyway possible.

Dick explained that he made his slides on the long airplane ride from SFO to Atlanta. In those days talks were made by writing on plastic transparencies with colored pens; my personal favorite were vis-a-vis markers. Dick said that he was concerned about the new type of pens that he had used. He did not use vis-a-vis, but he had used a new brand. We said that was fine. But he added that he was a bit concerned how well the slides would project, since they looked pretty pale. The pens apparently were not as good as old reliable vis-a-vis.

After lunch we jumped in Rich’s car, raced over to the conference hotel, and quickly found the room where Dick’s talk was going to be held. We immediately checked out the projector and more importantly Dick’s slides. He was right—the colors were extremely pale and the slides were nearly impossible to read. But it was way too late to redo them, since the talk was just about to start. Some of the audience were already entering the room.

We went forward—there was no other option. Rich gave a wonderful introduction of Karp, there was a long applause, and then Dick started his talk. He gave a great presentation. Whether the slides are perfect or not, Dick has a way of speaking and presenting his ideas that transcends everything else. Whether the red on a slide, for example, was pale or dark was unimportant. When Dick finished there was again a long applause. His talk was a huge success, but I believe he did throw the new pens away.

Let’s move on to discuss the talks, which were all quite different styles. Yet each was a masterpiece given by one of the leaders of the field.

What Makes an Algorithm Great?

Karp started by quoting me, twice. I was a bit embarrassed—and secretly pleased. The first quote was that “Algorithms are Tiny,” which I have discussed earlier. By the way the ideas in that discussion are joint with Lenore Blum.

Karp then began to discuss great algorithms based on various

criteria. The first list was historical: The Positional Number system of Alkarismi. The Chinese Remainder Theorem—which is really an algorithm. The Euclidean Algorithm. Gaussian Elimination. Impossible to argue with any on his list. Without positional numbers, I believe, that one could make the case that almost no modern algorithm is possible. The Chinese Remainder Theorem is one of my favorites, and the other two are clearly great.

criteria. The first list was historical: Karp then talked about several algorithms that he said were based on great ideas.

They were, Linear Programming Primality Testing Fast Matrix Product TSP Integer Factoring The famous simplex method for LP is still one of the best ways to solve linear programs. Karp pointed out that the ellipsoid method was a great theoretic result, but the interior-point methods were the first to solve LP’s in a way which was both in polynomial-time and practical. Karp gave a nice survey of the work on the TSP: he pointed out that Nicos Christofides’ wonderful -approximation algorithm for the metric TSP is still the best known. If you somehow do not know it check it out. It was discovered in 1976—that’s a lot of FOCS’s ago. Dick also pointed out that Volker Strassen’s famous fast matrix product algorithm was a surprise to the mathematical community.

They were, Karp is an expert in so many things, but perhaps his roots are in combinatorial optimization. So he went into some detail on the great algorithms from that area. Minimum Spanning Tree Shortest Path in Graphs For-Fulkerson Max-flow Algorithm Gale-Shapley Stable Marriage General Matching in Graphs In 1965 Jack Edmonds’ paper titled “Paths, Trees, and Flowers” not only showed how to do matching for non- bipartite graphs, but also laid out an informal basis of what later became P, NP, and co-NP. An incredible achievement for that point in time.

Karp next discussed randomized algorithms. I sometimes think we could not have a FOCS meeting if we disallowed the use of randomization. Primality Testing Volume of Convex Bodies Counting Matchings Min-Cut of Graphs String Matching Hashing All of these algorithms are great, and Karp spent some details on the volume algorithm of Alan Frieze and Ravi Kannan. He said nothing about the beautiful string matching algorithm that is due to Michael Rabin and himself. He was modest, but I am under no constraint. Their matching algorithm is one of the examples of the immense power of randomness. Their algorithm is theoretically fast and is practical. Actually it is more than practical, it is I believe the algorithm of choice for most packages. A wonderful algorithm, that would make my personal top ten.

Karp talked next about heuristic algorithms. These, he said, present an important challenge to theory. The central question, of course, is why do they work so well in practice? Local Search Shotgun Sequencing Algorithm Simulated Annealing Dick stated that Myers’ great work on sequencing was critical to the effort that first sequenced the human genome at Celera—then a company in competition with the NIH funded labs. What is so neat about Myers’ algorithm is that it required him to understand the structure of the human genome. The genome is not random, has structure, and that structure makes what Gene did so difficult.

Karp and complexity theory: NLOG is Closed Under Complement Undirected Connectivity is in DLOG Space is More Powerful Than Time Karp agreed with my earlier discussion that even complexity theory is really all about algorithms. He selected the above as the some of the most outstanding ones. John Hopcroft, Wolfgang Paul, and Leslie Valiant proved that deterministic time is in deterministic space . Their proof relies on several algorithmic ideas: the block-respecting method and the pebbling game strategy.

Karp only had time to list a number of great algorithms that show that theory can have impact on the “real-world.” All of the algorithms in his list have changed the world. Fast Fourier Transform RSA Encryption Miller-Rabin Primality Test Reed-Solomon Codes Lempel-Ziv Compression Page Rank of Google Consistent Hashing of Akamai Viterbi and Hidden Markov Models Smith-Waterman Spectral Low Rank Approximation Algorithms Binary Decision Diagrams Without these algorithms the world would stop: no web search, no digital music or movies, no security, and on and on.

Karp finally gave a short list of algorithms from the area of information transfer. Digital Fountain Codes CDMA Compressive Sensing of Images The first is a breakthrough method of Michael Luby that is a near optimal erasure code. In many situations information may be lost, rather than corrupted. Since it is lost the decoder knows that it is missing, which allows for a whole different type of error correcting code. These codes are extremely powerful: they are easy to encode and decode, and need relatively few extra bits of redundancy. Compressive Sensing is a relatively recent idea that is based on deep mathematics, yet is rapidly changing image compression. The creators are David Donoho, Emmanuel Candès, Justin Romberg, and Terence Tao—see this for many papers and articles from the area.



The talk was wonderful. Wonderful. In one hour we were carried from ancient times, past the dawn of modern computational complexity, then to the modern era, and the present—with a peek at the future. A masterful talk by the master. Later in the meeting Alon said that he could not imagine doing what Karp did. I completely agree.

Computational Aspects of Equilibria.

Yannakakis spoke about equilibria phenomena in game theory and economic theory. He started by saying that he had two hours worth of slides, but kindly finished nicely on time: close to the von Neumann Bound. John von Neumann once defined the perfect length of a talk as a micro-century, which works out to just about minutes.

Yannakakis first explained how many systems can evolve over time to a stable state. He spoke at some length about the classic example of neural nets and a 1982 theorem due to John Hopfield that proves that they always converge to a local minimum. The argument is based on showing that a certain potential function decreases and eventually reaches a local minimum.

Yannakakis then switched to a discussion that was mostly about various types of games. He discussed in detail the famous result of John Nash, proved in 1950, that any non-zero sum game always has at least one mixed equilibria point.

mixed equilibria point. Yannakakis’ key point was that Nash’s proof that mixed equilibria always exist was based on the famous Brouwer Fixed Point Theorem (BFPT). This theorem that is named after Luitzen Brouwer is also, as Mihalis said, used to prove that a whole host of other game/economic systems have a stable equilibrium point.

Yannakakis then showed the power of modern complexity theory. Just because the BFPT is used to prove something does not rule out the possibility that there is a proof that avoids it. But, this is not the case. He introduced three complexity classes: , , and . They capture respectively: general -player games, -player games, and finally games that have pure equilibria. A beautiful point is that he could use these classes to show that BFPT is equivalent to Nash’s famous theorem for multiple player games, and thus it is essential for the proof. The power of modern complexity should not be underestimated. Without the crisp notion of a “complexity class” I cannot see how any result like this could even be stated—let alone proved. Terrific.

, , and . They capture respectively: general -player games, -player games, and finally games that have pure equilibria. A beautiful point is that he could use these classes to show that BFPT is equivalent to Nash’s famous theorem for multiple player games, and thus it is for the proof. The power of modern complexity should not be underestimated. Without the crisp notion of a “complexity class” I cannot see how any result like this could even be stated—let alone proved. Terrific. Yannakakis also pointed out that solutions to multiple player games may not be rational numbers, in general. This was, by the way, known to Nash back in 1950. The problem with this is that then there may be no way to write down the exact solution to the problem. This plays havoc with complexity theory. Mihalis gave the analogy to the sum of square root problem: Is the following true, where the are natural numbers and is a rational number. It is a long standing open problem whether or not this can be solved in polynomial time. See this for a discussion that I had earlier on this topic.

Yannakakis finally gave us the usual news about the complexity classes: not much is known about their power. Oh well. The following diagram summarizes all that is known about them: (an arrow denotes inclusion)



His talk summarized quite neatly the relationship between equilibria problems and certain complexity classes. I wish that Yannakakis had more time to give more details—perhaps another talk.

Disjoint Paths, Isoperimetric Problems, and Graph Eigenvalues.

Alon started by pointing out that his first FOCS paper was at the . At that meeting, in 1984, he spoke on eigenvalues and expanders—he said he seemed to be stuck. He then pulled out a small piece of paper and proceeded to read,

I would like to thank the many FOCS program committees for their hard work and effort—except for the FOCS program committees of 1989, 2003, and 2006.

Even one of the greatest theorists in the world gets papers rejected from FOCS. This got a big laugh, yet I think it actually raises a serious question about the role of conferences. I know that is being discussed both on-line and off, but let’s move on to the rest of his talk and leave that discussion for another time and place.

Noga really gave highlights of two problems:

Alon first discussed a kind of routing game. He imagines that you have some fixed node degree expander graph. The game is this: you will be given a series of requests of the form: where this means that you are to find a set of edge disjoint paths from to for each . He proves that on a special type of expander that not only can this be done for such that but that it can be done on-line . That is there is an algorithm that selects the path from to without knowing the requests that will follow, This seems amazing to me. See the paper joint with Michael Capalbo for full details on how this works.

node degree expander graph. The game is this: you will be given a series of requests of the form: Alon then spoke on spines . Imagine dimension space divided into unit cubes. You are to select a subset of each cube so that this “spine” does not allow any non-trivial path that moves from cube to cube. In a sense the spines are a kind of geometric separator. Since separators play a key role in many parts of theory, it may come as no surprise that spines also are very useful. A deep result of Guy Kindler, Ryan O’Donnell, Anup Rao, and Avi Wigderson shows that a spine can have surface area bounded by where is the dimension of the space. However, the shape of their spine is essentially not known; it cannot be seen. The discussion here may help understand more about spines and their applications. Noga’s main result is a new uniform proof that solves this and other spine problems.

. Imagine dimension space divided into unit cubes. You are to select a subset of each cube so that this “spine” does not allow any non-trivial path that moves from cube to cube. In a sense the spines are a kind of geometric separator. Since separators play a key role in many parts of theory, it may come as no surprise that spines also are very useful. A deep result of Guy Kindler, Ryan O’Donnell, Anup Rao, and Avi Wigderson shows that a spine can have surface area bounded by where is the dimension of the space. However, the shape of their spine is essentially not known; it cannot be seen. The discussion here may help understand more about spines and their applications. Noga’s main result is a new uniform proof that solves this and other spine problems. Alon also had a simple piece of advice for all of us. He pointed out that we all use search engines to try to find mathematics that can help us in our research The trouble is that it is impossible to type in mathematical formulas into search engines. His suggestion is: give all your lemmas meaningful names. This may allow people to find your work, and then reference it. He gave an example of one of his lemmas: Lemma: (A simple discrete vertex isoperimetric inequality on the

Dirichlet boundary condition)

When I Googled this “name” the second hit was his paper—not too bad.

A wonderful talk, with light touches, and beautiful results; even though it contained many technical parts, I believe that most came away with the basic ideas that Noga wanted to get across.

Can (Theoretical Computer) Science Come to Grips With Consciousness?

Blum spoke about a life long quest, that started when he was six years old, to understand consciousness. Blum has a record of looking at old problems in new ways, of looking at new problems in his own way, of creating whole fields of study, and thus we should take his ideas on what is “consciousness” very seriously. Let’s call this the: What is consciousness problem. (WICP).

It is hard to really do justice to his wonderful talk, but I think there are a few high level points that I can get right:

Blum states that now may be the first time that real progress can be made on the WICP. This is due to the confluence of two events. The ability of researchers to use FMRI machines to watch a person’s brain as they think is relatively recent development. Second, is the maturing of a powerful theory called the Global Workspace Theory, which was created by Bernie Baars. I will not explain the theory, since I do not understand it. But Blum says that it has a very computational flavor, which may mean that theory can make an important contribution to the WICP.

Blum explained that when he was a graduate student he worked with some of the greats such as Warren McCulloch and Walter Pitts. They are famous for their notion of the McCulloch-Pitts neuron, which they proved could be the basis of a universal computing device. Today we study threshold functions and the complexity class that are modern versions of their pioneering work.

that are modern versions of their pioneering work. Blum pointed out a simple fact about the eye that I found fascinating: In the dark take a light source, and quickly move it around in front of you. You will see a path of light. This happens, Manny explained, because the eye responds only to motion. Then, keep the light source fixed and now move your head around. You will not see a path of light: you will see just a point of light. Somehow the eye and brain together can tell the difference between these two cases. Terrific.

Blum then shifted into a more theory oriented part of his talk. He explained what he called templates . They are his way of modeling how we solve problems. His stated goal is quite ambitious; if we could understand WICP he thinks that we might be able to make robots/agents that learn. The template model is a kind of tree. For example, suppose that you are trying to prove a theorem. Then, the root of the tree would contain a “hint” or some high-level idea that gets you thinking in the right way about your theorem. Below that would be more precise yet still informal pieces of information. Eventually, the leaves of the tree would contain more formal pieces that make up the actual proof.

. They are his way of modeling how we solve problems. His stated goal is quite ambitious; if we could understand WICP he thinks that we might be able to make robots/agents that learn. The template model is a kind of tree. For example, suppose that you are trying to prove a theorem. Then, the root of the tree would contain a “hint” or some high-level idea that gets you thinking in the right way about your theorem. Below that would be more precise yet still informal pieces of information. Eventually, the leaves of the tree would contain more formal pieces that make up the actual proof. Blum had a simple but great piece of advice to us all. If you are faced with a problem that you cannot solve, then modify the problem. Change it. Try another problem. He argued that this often may be interesting by itself, and sometimes may shed light on the original problem. Great advice.

Blum ended with a pretty puzzle. Is there a irrational number so that for a rational ? The answer is yes, and he explained how his templates could guide you to the solution to his problem.

A wonderful talk, from one of the great visionaries of the field.

Open Problems

Before turning to some open problems I want to thank the following people for making Theory day such a great success: Dani Denton, Milena Mihail, Dan Spielman, and Robin Thomas. Thanks for all your hard work.

Robin pointed out that it was also ACO’s anniversary, and all of the talks should be online later this week.

Now some open problems that each speaker implicitly raised:

Karp: What are your favorite algorithms? Do you agree with Dick’s lists?

Yannakakis: What are the relative powers of the complexity classes discussed? There is a rumor that part of this may be resolved—more on this soon.

Alon: What does a spine really look like? Can the same bounds be found with spines that one can actually “see?”