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ACM Symposium on Theory of Computing (STOC)

Holographic algorithm with matchgates is universal for planar #CSP over boolean domain

Jin‐Yi Cai, Zhiguo Fu

Published Jun 15, 2017• 8 citations
Publisher

Abstract

We prove a complexity classification theorem that classifies all counting constraint satisfaction problems (#CSP) over Boolean variables into exactly three classes: (1) Polynomial-time solvable; (2) #P-hard for general instances, but solvable in polynomial-time over planar structures; and (3) #P-hard over planar structures. The classification applies to all finite sets of complex-valued, not necessarily symmetric, constraint functions on Boolean variables. It is shown that Valiant's holographic algorithm with matchgates is universal strategy for all problems in class (2).

DOI
10.1145/3055399.3055405

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•Aug 14, 2010•34 citations
We prove a complexity classification theorem that classifies all counting constraint satisfaction problems (#CSP) over Boolean variables into exactly three classes: (1) Polynomial-time solvable; (2) #P-hard for general instances, but solvable in polynomial-time over planar structures; and (3) #P-hard over planar structures. The classification applies to all finite sets of complex-valued, not necessarily symmetric, constraint functions on Boolean variables. It is shown that Valiant's holographic algorithm with matchgates is universal strategy for all problems in class (2).
We prove a complexity classification theorem that classifies all counting constraint satisfaction problems (\#CSP) over Boolean variables into exactly three classes: (1) polynomial-time solvable; (2) \#P-hard for general instances but solvable in polynomial time over planar structures; and (3) \#P-hard over planar structures. The classification applies to all finite sets of local, not necessarily symmetric, constraint functions on Boolean variables that take algebraic complex values. It is shown that Valiant's holographic algorithm with matchgates is a universal strategy for all problems in class (2).
Holographic Algorithm with Matchgates Is Universal for Planar #\##CSP Over Boolean Domain
We prove a complexity classification theorem that classifies all counting constraint satisfaction problems (#\##CSP) over Boolean variables into exactly three categories: (1) Polynomial-time tractable; (2) #\##P-hard for general instances, but solvable in polynomial-time over planar graphs; and (3) #\##P-hard over planar graphs. The classification applies to all sets of local, not necessarily symmetric, constraint functions on Boolean variables that take complex values. It is shown that Valiant's holographic algorithm with matchgates is a universal strategy for all problems in category (2).
Valiant introduced matchgate computation and holographic algorithms. A number of seemingly exponential time problems can be solved by this novel algorithmic paradigm in polynomial time. We show that, in a very strong sense, matchgate computations and holographic algorithms based on them provide a universal methodology to a broad class of counting problems studied in the statistical physics community for decades. They capture precisely those problems which are #P-hard on general graphs but computable in polynomial time on planar graphs. More precisely, we prove complexity dichotomy theorems in the framework of counting CSP problems. The local constraint functions take Boolean inputs and can be arbitrary real-valued symmetric functions. We prove that every problem in this class belongs to precisely three categories: (1) those which are tractable (i.e., polynomial time computable) on general graphs, or (2) those which are #P-hard on general graphs but tractable on planar graphs, or (3) those which are #P-hard even on planar graphs. The classification criteria are explicit. Moreover, problems in category (2) are tractable on planar graphs precisely by holographic algorithms with matchgates.
Valiant introduced matchgate computation and holographic algorithms. A number of seemingly exponential time problems can be solved by this novel algorithmic paradigm in polynomial time. We show that, in a very strong sense, matchgate computations and holographic algorithms based on them provide a universal methodology to a broad class of counting problems studied in statistical physics community for decades. They capture precisely those problems which are #P-hard on general graphs but computable in polynomial time on planar graphs. More precisely, we prove complexity dichotomy theorems in the framework of counting CSP problems. The local constraint functions take Boolean inputs, and can be arbitrary real-valued symmetric functions. We prove that, every problem in this class belongs to precisely three categories: (1) those which are tractable (i.e., polynomial time computable) on general graphs, or (2) those which are \#P-hard on general graphs but ractable on planar graphs, or (3) those which are #P-hard even on planar graphs. The classification criteria are explicit. Moreover, problems in category (2) are tractable on planar graphs precisely by holographic algorithms with matchgates.
Valiant introduced match gate computation and holographic algorithms. A number of seemingly exponential time problems can be solved by this novel algorithmic paradigm in polynomial time. We show that, in a very strong sense, match gate computations and holographic algorithms based on them provide a universal methodology to a broad class of counting problems studied in statistical physics community for decades. They capture precisely those problems which are #P-hard on general graphs but computable in polynomial time on planar graphs. More precisely, we prove complexity dichotomy theorems in the framework of counting CSP problems. The local constraint functions take Boolean inputs, and can be arbitrary real-valued symmetric functions. We prove that, every problem in this class belongs to precisely three categories: (1) those which are tractable (i.e., polynomial time computable) on general graphs, or (2) those which are #P-hard on general graphs but ractable on planar graphs, or (3) those which are #P-hard even on planar graphs. The classification criteria are explicit. Moreover, problems in category (2) are tractable on planar graphs precisely by holographic algorithms with matchgates.
Holant problems are a general framework to study counting problems. Both counting Constraint Satisfaction Problems (#CSP) and graph homomorphisms are special cases. We prove a complexity dichotomy theorem for Holant ∗ (F), where F is a set of constraint functions on Boolean variables and output complex values. The constraint functions need not be symmetric functions. We identify four classes of problems which are polynomial time computable; all other problems are proved to be #P-hard. The main proof technique and indeed the formulation of the theorem use holographic algorithms and reductions. By considering these counting problems over the complex domain, we discover surprising new tractable classes, which are associated with isotropic vectors, i.e., a (non-zero) vector whose inner product with itself is zero. 1