A major open problem in proof complexity is to prove superpolynomial lower bounds for AC0[p]-Frege proofs. This system is the analog of AC0 [p], the class of bounded depth circuits with prime modular counting gates. Despite strong lower bounds for this class dating back thirty years ([28, 30]), there are no significant lower bounds for AC0 [p]-Frege. Significant and extensive degree lower bounds have been obtained for a variety of subsystems of AC0[p]-Frege, including Nullstellensatz ([3]), Polynomial Calculus ([9]), and SOS ([14]). However to date there has been no progress on AC0 [p]-Frege lower bounds. In this paper we study constant-depth extensions of the Polynomial Calculus [13]. We show that these extensions are much more powerful than was previously known. Our main result is that small depth (≤ 43) Polynomial Calculus (over a sufficiently large field) can polynomially effectively simulate all of the well-studied semialgebraic proof systems: Cutting Planes, Sherali-Adams, Sum-of-Squares (SOS), and Positivstellensatz Calculus (Dynamic SOS). Additionally, they can also quasi-polynomially effectively simulate AC0[q]-Frege for any prime q independent of the characteristic of the underlying field. They can also effectively simulate TC0-Frege if the depth is allowed to grow proportionally. Thus, proving strong lower bounds for constant-depth extensions of Polynomial Calculus would not only give lower bounds for AC0 [p]-Frege, but also for systems as strong as TC0-Frege. 
                        more » 
                        « less   
                    
                            
                            Stabbing Planes
                        
                    
    
            We develop a new semi-algebraic proof system called Stabbing Planes which formalizes modern branch-and-cut algorithms for integer programming and is in the style of DPLL-based modern SAT solvers. As with DPLL there is only a single rule: the current polytope can be subdivided by branching on an inequality and its “integer negation.” That is, we can (non-deterministically choose) a hyperplane ax ≥ b with integer coefficients, which partitions the polytope into three pieces: the points in the polytope satisfying ax ≥ b, the points satisfying ax ≤ b, and the middle slab b − 1 < ax < b. Since the middle slab contains no integer points it can be safely discarded, and the algorithm proceeds recursively on the other two branches. Each path terminates when the current polytope is empty, which is polynomial-time checkable. Among our results, we show that Stabbing Planes can efficiently simulate the Cutting Planes proof system, and is equivalent to a tree-like variant of the R(CP) system of Krajicek [54]. As well, we show that it possesses short proofs of the canonical family of systems of F_2-linear equations known as the Tseitin formulas. Finally, we prove linear lower bounds on the rank of Stabbing Planes refutations by adapting lower bounds in communication complexity and use these bounds in order to show that Stabbing Planes proofs cannot be balanced. 
        more » 
        « less   
        
    
    
                            - PAR ID:
- 10343001
- Date Published:
- Journal Name:
- ArXivorg
- Volume:
- arxiv:2017
- Issue:
- arxiv:1710.03219v2
- ISSN:
- 2331-8422
- Page Range / eLocation ID:
- 1-29
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
- 
            
- 
            Dubois, Catherine; Kerber, Manfred (Ed.)Solving a sparse linear system of the form Ax = b is a common engineering task, e.g., as a step in approximating solutions of differential equations. Inverting a large matrix A is often too expensive, and instead engineers rely on iterative methods, which progressively approximate the solution x of the linear system in several iterations, where each iteration is a much less expensive (sparse) matrix-vector multiplication. We present a formal proof in the Coq proof assistant of the correctness, accuracy and convergence of one prominent iterative method, the Jacobi iteration. The accuracy and convergence properties of Jacobi iteration are well-studied, but most past analyses were performed in real arithmetic; instead, we study those properties, and prove our results, in floatingpoint arithmetic. We then show that our results are properly reflected in a concrete implementation in the C language. Finally, we show that the iteration will not overflow, under assumptions that we make explicit. Notably, our proofs are faithful to the details of the implementation, including C program semantics and floating-point arithmetic.more » « less
- 
            Dubois, Catherine; Kerber, Manfred (Ed.)Solving a sparse linear system of the form Ax = b is a common engineering task, e.g., as a step in approximating solutions of differential equations. Inverting a large matrix A is often too expensive, and instead engineers rely on iterative methods, which progressively approximate the solution x of the linear system in several iterations, where each iteration is a much less expensive (sparse) matrix-vector multiplication. We present a formal proof in the Coq proof assistant of the correctness, accuracy and convergence of one prominent iterative method, the Jacobi iteration. The accuracy and convergence properties of Jacobi iteration are well-studied, but most past analyses were performed in real arithmetic; instead, we study those properties, and prove our results, in floatingpoint arithmetic. We then show that our results are properly reflected in a concrete implementation in the C language. Finally, we show that the iteration will not overflow, under assumptions that we make explicit. Notably, our proofs are faithful to the details of the implementation, including C program semantics and floating-point arithmetic.more » « less
- 
            We explore algorithms and limitations for sparse optimization problems such as sparse linear regression and robust linear regression. The goal of the sparse linear regression problem is to identify a small number of key features, while the goal of the robust linear regression problem is to identify a small number of erroneous measurements. Specifically, the sparse linear regression problem seeks a k-sparse vector x ∈ Rd to minimize ‖Ax − b‖2, given an input matrix A ∈ Rn×d and a target vector b ∈ Rn, while the robust linear regression problem seeks a set S that ignores at most k rows and a vector x to minimize ‖(Ax − b)S ‖2. We first show bicriteria, NP-hardness of approximation for robust regression building on the work of [OWZ15] which implies a similar result for sparse regression. We further show fine-grained hardness of robust regression through a reduction from the minimum-weight k-clique conjecture. On the positive side, we give an algorithm for robust regression that achieves arbitrarily accurate additive error and uses runtime that closely matches the lower bound from the fine-grained hardness result, as well as an algorithm for sparse regression with similar runtime. Both our upper and lower bounds rely on a general reduction from robust linear regression to sparse regression that we introduce. Our algorithms, inspired by the 3SUM problem, use approximate nearest neighbor data structures and may be of independent interest for solving sparse optimization problems. For instance, we demonstrate that our techniques can also be used for the well-studied sparse PCA problem.more » « less
- 
            Narodytska, Nina; Rümmer, Philipp (Ed.)Clausal proofs, particularly those based on the deletion resolution asymmetric tautology (DRAT) proof system, are widely used by Boolean satisfiability solvers for expressing proofs of unsatisfiability. Their success stems from their simplicity and scalability. When solvers go beyond pure propositional reasoning, however, generating clausal proofs becomes more difficult. Solvers that employ pseudo-Boolean reasoning, including cutting-planes operations, can express proofs in the VeriPB proof system, but its adoption is not widespread. We introduce PBIP (Pseudo-Boolean Implication Proof), a framework that provides an intermediate representation between VeriPB and clausal proofs. We also introduce a toolchain comprising 1) a VeriPB-to-PBIP translator that performs proof trimming and optimization, and 2) a PBIP-to-LRAT translator that makes use of proof-generating operations on ordered binary decision diagrams (BDDs) to generate clausal proofs in LRAT format, a variant of the DRAT that allows efficient checking. We demonstrate the viability of our approach, the effectiveness of our trimming, and the performance of our clausal proof generator on a set of native PB benchmarks and compare our approach to direct checking of VeriPB proofs.more » « less
 An official website of the United States government
An official website of the United States government 
				
			 
					 
					
 
                                    