Counting CSPs Research Articles

Special Section on the Forty-Ninth Annual ACM Symposium on the Theory of Computing (STOC 2017)

This issue of SICOMP contains ten specially selected papers from STOC 2017, the Forty-ninth Annual ACM Symposium on the Theory of Computing, which was held June 19--23 in Montreal, Canada. The papers here were chosen to represent the range and quality of the STOC program. These papers have been revised and extended by their authors and subjected to the standard thorough reviewing process of SICOMP. The program committee for STOC 2017 consisted of Nina Balcan, Allan Borodin, Keren Censor-Hillel, Edith Cohen, Artur Czumaj, Yevgeniy Dodis, Andrew Drucker, Nick Harvey, Monika Henzinger, Russell Impagliazzo, Ken-ichi Kawarabayashi, Ravi Kumar, James R. Lee, Katrina Ligett, Aleksander Mądry, Cristopher Moore, Jelani Nelson, Eric Price, Amit Sahai, Jared Saia, Shubhangi Saraf, Alexander Sherstov, Mohit Singh, and Gábor Tardos. The program chair was Valerie King. Included in this issue are the following papers: ``Short Presburger Arithmetic Is Hard," by Danny Nguyen and Igor Pak, proves that the satisfiability of short sentences in Presburger arithmetic with $m+2$ alternating quantifiers is $\Sigma^{{P}}_m$-complete or $\Pi^{{P}}_m$-complete when the first quantifier is $\exists$ or $\forall$, respectively. ``An Efficient Reduction from Two-Source to Nonmalleable Extractors: Achieving Near-Logarithmic Min-Entropy," by Avraham Ben-Aroya, Dean Doron, and Amnon Ta-Shma, gets an explicit bipartite Ramsey graph (or a twosource extractor) for sets of size 2$k$ for $k = O(\log n \log \log n)$, using the currently best explicit nonmalleable extractors. ``Holographic Algorithm with Matchgates Is Universal for Planar \#CSP over Boolean Domain," by Jin-Yi Cai and Zhiguo Fu, classifies all counting CSPs over Boolean variables into one of three categories: polynomial-time tractable, \#P-hard for general instances but solvable in polynomial time over planar graphs, and \#P-hard over planar graphs. ``Deciding Parity Games in Quasipolynomial Time," by Cristian S. Calude, Sanjay Jain, Bakhadyr Khoussainov, Wei Li, and Frank Stephan, shows the parameterized parity game, with $n$ nodes and $m$ priorities, is in the class of fixed parameter tractable problems when parameterized over $m$. ``New Hardness Results for Routing on Disjoint Paths," by Julia Chuzhoy, David H. K. Kim, and Rachit Nimavat, proves that node-disjoint paths is $2^{\Omega(\sqrt{\log n})}$-hard to approximate, unless all problems in NP have algorithms with running time $n^{O(\log n)}$. ``A Weighted Linear Matroid Parity Algorithm," by Satoru Iwata and Yusuke Kobayashi, presents a combinatorial, deterministic, strongly polynomial-time algorithm for the weighted linear matroid parity problem. ``Targeted Pseudorandom Generators, Simulation Advice Generators, and Derandomizing Logspace," by William M. Hoza and Chris Umans, shows that $\mathbf{BPL} \subseteq \bigcap_{\alpha > 0} {DSPACE}(\log^{1 + \alpha} n)$, assuming that for every derandomization result for log-space algorithms there is a pseudorandom generator strong enough to nearly recover the derandomization by iterating over all seeds and taking a majority vote. ``Approximating Rectangles by Juntas and Weakly Exponential Lower Bounds for LP Relaxations of CSPs," by Pravesh K. Kothari, Raghu Meka, and Prasad Raghavendra, shows that for CSPs, subexponential size LP relaxations are as powerful as $n^{\Omega(1)}$-rounds of the Sherali--Adams LP hierarchy. ``Equivocating Yao: Constant-Round Adaptively Secure Multiparty Computation in the Plain Model," by Ran Canetti, Oxana Poburinnaya, and Muthuramakrishnan Venkitasubramaniam, defines a new type of encryption and shows that Yao's garbling scheme, implemented with this encryption mechanism, is secure against adaptive adversaries. ``Geodesic Walks in Polytopes," by Yin Tat Lee and Santosh Vempala, introduces the geodesic walk for sampling Riemannian manifolds and applies it to the problem of generating uniform random points from the interior of polytopes in ${\mathbb{R}}^{n}$ specified by m inequalities; the resulting sampling algorithm for polytopes mixes in $O^{*}(mn^{\frac{3}{4}})$ steps. We thank the authors, the STOC 2017 program committee, the STOC 2017 external reviewers, and the SICOMP referees for all of their hard work. Andy Drucker, Ravi Kumar, Amit Sahai, Mohit Singh, Guest editors

Boolean approximate counting CSPs with weak conservativity, and implications for ferromagnetic two-spin

We analyse the complexity of approximate counting constraint satisfactions problems #CSP(F), where F is a set of nonnegative rational-valued functions of Boolean variables. A complete classification is known if F contains arbitrary unary functions. We strengthen this result by fixing any permissive strictly increasing unary function and any permissive strictly decreasing unary function, and requiring only those to be in F. The resulting classification is employed to characterise the complexity of a wide range of two-spin problems, fully classifying the ferromagnetic case. Furthermore, we also consider what happens if only the pinning functions are assumed to be in F. We show that any set of functions for which pinning is not sufficient to recover the two kinds of permissive unaries must either have a very simple range, or must satisfy a certain monotonicity condition. We exhibit a non-trivial example of a set of functions satisfying the monotonicity condition.

The complexity of approximating conservative counting CSPs

We study the complexity of the approximate weighted counting constraint satisfaction problem #CSP(F). In the conservative case, where F contains all unary functions, a classification is known over the Boolean domain; we extend this to arbitrary finite domains. We show that if F is “weakly log-modular”, then #CSP(F) is in FP. Otherwise, it is at least as difficult to approximate as #BIS (counting independent sets in bipartite graphs). #BIS is complete for the complexity class #RHΠ1, and believed to be intractable. We further sub-divide the #BIS-hard case: if F is “weakly log-supermodular”, #CSP(F) is as easy as a Boolean log-supermodular weighted #CSP; otherwise, we show that it is NP-hard to approximate. Finally, we give a full trichotomy for the arity-2 case: #CSP(F) is in FP, is #BIS-equivalent, or is equivalent to #SAT (approximately counting the satisfying assignments of Boolean CNF formulas). We also discuss algorithmic aspects of our classification.

Approximate counting for complex-weighted Boolean constraint satisfaction problems

Constraint satisfaction problems (or CSPs) have been extensively studied in, for instance, artificial intelligence, database theory, graph theory, and statistical physics. From a practical viewpoint, it is beneficial to approximately solve those CSPs. When one tries to approximate the total number of truth assignments that satisfy all Boolean-valued constraints for (unweighted) Boolean CSPs, there is a known trichotomy theorem by which all such counting problems are neatly classified into exactly three categories under polynomial-time (randomized) approximation-preserving reductions. In contrast, we obtain a dichotomy theorem of approximate counting for complex-weighted Boolean CSPs, provided that all complex-valued unary constraints are freely available to use. It is the expressive power of free unary constraints that enables us to prove such a stronger, complete classification theorem. This discovery makes a step forward in the quest for the approximation-complexity classification of all counting CSPs. To deal with complex weights, we employ proof techniques of factorization and arity reduction along the line of solving Holant problems. Moreover, we introduce a novel notion of T-constructibility that naturally induces approximation-preserving reducibility. Our result also gives an approximation analogue of the dichotomy theorem on the complexity of exact counting for complex-weighted Boolean CSPs.

A dichotomy theorem for the approximate counting of complex-weighted bounded-degree Boolean CSPs

We determine the computational complexity of approximately counting the total weight of variable assignments for every complex-weighted Boolean constraint satisfaction problem (or CSP) with any number of additional unary (i.e., arity 1) constraints, particularly, when degrees of input instances are bounded from above by a fixed constant. All degree-1 counting CSPs are obviously solvable in polynomial time. When the instance’s degree is more than two, we present a dichotomy theorem that classifies all counting CSPs admitting free unary constraints into exactly two categories. This classification theorem extends, to complex-weighted problems, an earlier result on the approximation complexity of unweighted counting Boolean CSPs of bounded degree. The framework of the proof of our theorem is based on a theory of signature developed from Valiant’s holographic algorithms that can efficiently solve seemingly intractable counting CSPs. Despite the use of arbitrary complex weight, our proof of the classification theorem is rather elementary and intuitive due to an extensive use of a novel notion of limited T-constructibility. For the remaining degree-2 problems, in contrast, they are as hard to approximate as Holant problems, which are a generalization of counting CSPs.