# UGC & SDP, or, Are there any more polynomial time algorithms? Ryan O’Donnell Microsoft Research.

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UGC & SDP, or, Are there any more polynomial time algorithms? Ryan O’Donnell Microsoft Research

Maximum Matching [Edmonds ’65] multiplication, GCD, determinant [prehistory] Dynamic Programming [Bellman ’53] # Perfect Matchings in planar graphs [Kasteleyn ’67] Primality [Solovay, Strassen ’77] Linear Programming [Khachiyan ’79] Semidefinite Programming [Grötschel, Lovász, Schrijver ’81] (Ellipsoid method) [Nemirovski, Yudin ’76] 3SAT [Cook ’71] Karp’s 21 Problems [Karp ’72] A few more [Levin ’73] Garey & Johnson’s 300+ problems [1979] reduction Integer Programming, fixed dimension [Lenstra ’83] Markov Chain Monte Carlo [Jerrum, Sinclair / Dyer, Frieze, Kannan ’88] Recognizing minor-closed graph families [Robertson, Seymour ’95] PNP-hard Factoring Discrete Log Graph Isomorphism Lattices: SVP, CVP Nash equilibria / BPP

Problems not known in P or NP-hard “If your problem belongs to NP and you cannot prove that it is NP-hard, it may be an “NP- intermediate” problem… However, very few natural problems are currently counted as good candidates for such intermediate status: factoring, discrete logarithm, graph- isomorphism, and several problems relating to lattice bases form a very representative list… The vast majority of natural problems in NP have resolved themselves as being either in P or NP-complete. Unless you uncover a specific connection to one of those four intermediate problems, it is more likely offhand that your problem simply needs more work.” “But, most natural languages in NP have been shown to be either in P or NP-complete. Here are two important exceptions: 1. Graph Isomorphism… 2. Factoring and related problems such as Discrete Log and Primality. (Note: many people believe Primality is in P.)” Allender, Loui, Regan, Micah Adler, in my 1998 Handbook on Algorithms Intro to Complexity Theory and Theory of Computation: CSC 2041 class:

Problems not known in P or NP-hard But… 1000-color a 3-colorable graph Given a graph, say YES if it has a vertex cover using 51% of the vertices, say NO if every vertex cover requires 99% of the vertices. 90%-approximate MAX-CUT 95%-approximate MAX-2SAT Distinguish (1−  )-satisfiability and (1/q)  /2 -satisfiability for MAX-2LIN(mod q) (log log log n) -approximate Sparsest Cut (1−1/2q)-approximate MAX-q-CUT (2k/2k)-approximate MAX-k-AND 1.49-approximate metric TSP 1.54-approximate minimum Steiner tree (.1 log n)-approximate asymm. metric TSP 1.3-approximate minimum multiway cut 1.51-approximate minimum uncapacitated metric facility location (log n)-approximate bandwidth of graphs (log n) 1/3 -approximating 0-Extension.92-approximating MAX-E3-Set-Splitting 1.5-approximate rectangle tiling O(1)-approximate minimum linear arrangement O(1)-approximate minimum feedback arc.51-approximate maximum betweenness … In addition, there are dozens of learning problems, such as… learning poly-sized DNF … In addition, there are dozens of learning problems, such as… learning poly-sized DNF …

Thesis, part 1: If we are going to say with a straight face that the theory of NP-completeness is very successful, we had better classify almost all natural open hardness-of-approximation problems.

Problems not known in P or NP-hard But… 1000-color a 4-colorable graph Given a graph, say YES if it has a vertex cover using 51% of the vertices, say NO if every vertex cover requires 99% of the vertices. 90%-approximate MAX-CUT 95%-approximate MAX-2SAT Distinguish (1−  )-satisfiability and (1/q)  /2 -satisfiability for MAX-2LIN(mod q) (log log log n) -approximate Sparsest Cut (1−1/2q)-approximate MAX-q-CUT (2k/2k)-approximate MAX-k-AND 1.49-approximate metric TSP 1.54-approximate minimum Steiner tree (.1 log n)-approximate asymm. metric TSP 1.3-approximate minimum multiway cut 1.51-approximate minimum uncapacitated metric facility location (log n)-approximate bandwidth of graphs (log n) 1/3 -approximating 0-Extension.92-approximating MAX-E3-Set-Splitting 1.5-approximate rectangle tiling O(1)-approximate minimum linear arrangement O(1)-approximate minimum feedback arc.51-approximate maximum betweenness … Distinguish (1−  )-satisfiability and  -satisfiability of Unique-Label-Cover

Christos Papadimitriou, 2001: “Together with factoring, [Nash equilibria is] the most important concrete open question on the boundary of P today.” Thesis, part 2: Besides factoring, approximability of Unique Label Cover is the most important concrete open question on the boundary of P today.

Thesis, part 3: The UGC situation is win-win. In particular, if UGC is false, we get a fantastic new algorithm no one has thought of before, at least as exciting as Goemans-Williamson’s use of SDP or Arora-Rao-Vazirani.

Remainder of the talk Thesis, part 4: Something fishy is going on, connecting UGC, SDP algorithms, SDP integrality gaps, boolean/Gaussian Fourier analysis.

Remainder of the talk UGC seems crucial for proving hardness of “2-variable constraint satisfaction problems”. Such problems have natural SDP relaxations. It seems that the sharp SDP integrality gaps occur in Gaussian space. These gaps can usually be translated into analogous “Dictator Tests” (“Long Code Tests”), which are the main ingredient in the standard recipe for UGC-hardness results.

Max-Cut-Gain Max-Cut: Given:an undirected graph on N vertices with nonnegative edge-weights summing to 1. Task: cut the vertices into two parts, maximizing the amount of weight crossing the cut. Trivial fact: the Max-Cut is always at least ½. Max-Cut-Gain: Measure your success not by the amount you cut, but by the amount you cut in excess of ½. twice that, so it’s in [0,1]

Alternate formulation Put the edge-weights in a symmetric, nonnegative matrix W. Max-Cut-Gain: Let A = − N ¢ W. where: h, i is the “inner product” on R 1 – just multiplication [N] is treated as a probability space with uniform dist. A is a negated “probability operator” in the space L 2 ([N])

SDP relaxation (where B d is the unit ball in R d )

Charikar-Wirth Max-Cut-Gain alg. Solve the SDP, getting f : [N] ! B d with Pick g at random from (R d,  ) (d-dim. Gaussian dist.) For each i 2 [N], look at h f (i), g i ; round into [−1,1] via −1 1 −t−t h f (i), g i

Charikar-Wirth Max-Cut-Gain alg. When there is an SDP solution f : [N] ! B d with value , CW gives an actual solution f : [N] ! B 1 with value  (  / log(1/  )). To show this rounding procedure is tight is to show an “SDP gap”.

SDP relaxation where (X,  ) is any probability space. In particular, we will look for an SDP gap in Gaussian space: X = R d,  = , d-dimensional Gaussian measure.

Hermite expansions Every function f : (R d,  ) ! B d has a Hermite expansion where the Hermite coefficients are in B d the polynomials {H S } S 2 N d are orthogonal H 0 = 1, H e i (x) = x i, where e i = (0, 0, …, 1, …, 0, 0).

Our operator A  Let P 1 denote the linear operator “ projection to level 1 ”: Now let A  =  P 1 − (id − P 1 ). Using orthogonality of the Hermite polynomials, we get: For an SDP gap, we have to determine the maximum of this for f : (R d,  ) ! B d and for f : (R d,  ) ! B 1. NOT the negative of a probability operator with 0’s on diagonal. However, you can hack around this.

f : (R d,  ) ! B d It’s well-known that almost all probability mass in the d-dimensional Gaussian distribution is concentrated around the spherical shell of radius. So the function f (x) = x / is basically okay. This function has, and all its weight is at level 1. So

f : (R d,  ) ! B 1 Hermite coefficients are just numbers in R now. Write ( P 1 f )(x) =  a i x i, On random input x, P 1 f is just a 1-d Gaussian with variance  2. Heavy tail property of Gaussians: a Gaussian with variance  2 goes above 2 in abs. value with probability at least exp(−O(2 2 )/  2 ). Since | f (x) | · 1, whenever this happens there is a contribution of at least 1 to

f : (R d,  ) ! B 1 Hence It’s easy to see that the optimum occurs when In fact, one can show the optimal f is exactly the Charikar-Wirth rounding function! This proves an upper bound of O(  / log(1/  )), and gives an optimal  vs. O(  / log(1/  )) SDP gap in Gaussian space.

UGC-hardness? The “standard recipe” for proving UGC-hardness: Instead of an SDP gap, give something sort of similar – a Dictator Test (or “Long Code” Test): Look at functions f : {−1, 1} d ! [−1,1]. Distinguish: “dictator” functions, f (x) = x i, from functions far from every dictator.

f : {−1, 1} d ! [−1,1] Such functions f have an orthogonal “Fourier expansion”, In particular, W 0 = 1, W e i (x) = x i, as before. We can define A  as before, and now the proof of the Dictator Test is virtually identical to the proof of the SDP gap. Note that each dictator f (x) = x i has all its weight at level 1, as in the first case before,

f : {−1, 1} d ! [−1,1] For general f, ( P 1 f )(x) =  a i x i, where a i measures how close f is to the i-dictator. If f is far from all dictators, all a i ’s are very small in absolute value. In this case, on random input x 2 {−1, 1} d, by the CLT, (P 1 f )(x) acts very much like a Gaussian; in particular it has the same Heavy Tail property.

UGC-hardness of Max-Cut-Gain Conclusion: Given a weighted graph with maximum cut ½ + , it is NP-hard to find a cut with weight ½ + O(  / log(1/  )) assuming UGC. I.e., Charikar-Wirth is tight subject to UGC. Note: with KKMO’04, this essentially closes all aspects of UGC- hardness of approximating Max-Cut.

Recap We took the Max-Cut-Gain problem (a 2-variable CSP) Set up the SDP relaxation Found a natural, tight SDP gap in Gaussian space Converted it to a Dictator Test to get UGC-hardness. This recipe also works (but is more difficult) for UGC-hardness of MAX-CUT, Vertex-Cover, … What’s the deal…?

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