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An arbitrary starting variable dimension algorithm is proposed to compute an integer point of an n-dimensional simplex. It is based on an integer labeling rule and a triangulation of R^n. The algorithm consists of two interchanging phases. The first phase of the algorithm is a variable dimension algorithm, which generates simplices of varying dimensions, and the second phase of the algorithm forms a full-dimensional pivoting procedure, which generates n-dimensional simplices. The algorithm varies from one phase to the other. When the matrix defining the simplex is in the so-called canonical form, starting at an arbitrary integer point, the algorithm within a finite number of iterations either yields an integer point of the simplex or proves that no such point exists. [PUBLICATION ABSTRACT]

\"Satisfiability (SAT) related topics have attracted researchers from various disciplines: logic, applied areas such as planning, scheduling, operations research and combinatorial optimization, but also theoretical issues on the theme of complexity and much more, they all are connected through SAT. My personal interest in SAT stems from actual solving: The increase in power of modern SAT solvers over the past 15 years has been phenomenal. It has become the key enabling technology in automated verification of both computer hardware and software. Bounded Model Checking (BMC) of computer hardware is now probably the most widely used model checking technique. The counterexamples that it finds are just satisfying instances of a Boolean formula obtained by unwinding to some fixed depth a sequential circuit and its specification in linear temporal logic. Extending model checking to software verification is a much more difficult problem on the frontier of current research. One promising approach for languages like C with finite word-length integers is to use the same idea as in BMC but with a decision procedure for the theory of bit-vectors instead of SAT. All decision procedures for bit-vectors that I am familiar with ultimately make use of a fast SAT solver to handle complex formulas. Decision procedures for more complicated theories, like linear real and integer arithmetic, are also used in program verification. Most of them use powerful SAT solvers in an essential way. Clearly, efficient SAT solving is a key technology for 21st century computer science. I expect this collection of papers on all theoretical and practical aspects of SAT solving will be extremely useful to both students and researchers and will lead to many further advances in the field.\"--Edmund Clarke (FORE Systems University Professor of Computer Science and Professor of Electrical and Computer Engineering at Carnegie Mellon University, winner of the 2007 A.M. Turing Award).

An enhanced concept of sub-optimal reverse Horn fraction of a CNF-formula was introduced in [18]. It was shown that this fraction is very useful in effectively (almost) separating 3-colorable random graphs with fixed node-edge density from the non-3-colorable ones. A correlation between this enhanced sub-optimal reverse Horn fraction and satisfiability of random 3-SAT instances with a fixed density was observed. In this paper, we present experimental evidence that this correlation scales to larger-sized instances and that it extends to solver performances as well, both of complete and incomplete solvers. Furthermore, we give a motivation for various phases in the algorithm aHS, establishing the enhanced sub-optimal reverse Horn fraction, and we present clear evidence for the fact that the observed correlations are stronger than correlations between satisfiability and sub-optimal MAXSAT-fractions established similarly to the enhanced sub-optimal reverse Horn fraction. The latter observation is noteworthy because the correlation between satisfiability and the optimal MAXSAT-fraction is obviously 100%.

In this note we propose to use the volume of elliptic approximations of satisfiability problems as a measure for computing weighting coefficients of clauses of different lengths. For random 3-SAT formula it is confirmed experimentally that, when applied in a DPLL algorithm with a branching strategy that is based on the ellipsoids as well, the weight deduced yields better results than the weights that are used in previous studies.

We propose an algorithm to find an integer point of a simplex. The algorithm is based on an integer labeling rule and a triangulation of the space. Starting at an integer point, the algorithm leaves it along one of n rays and generates a sequence of adjacent simplices of varying dimension. Within a finite number of iterations, the algorithm either yields an integer point of the simplex or proves that no such point exists.

We derive a semidefinite relaxation of the satisfiability (SAT) problem and discuss its strength. We give both the primal and dual formulation of the relaxation. The primal formulation is an eigenvalue optimization problem, while the dual formulation is a semidefinite feasibility problem. We show that using the relaxation, a proof of the unsatisfiability of the notorious pigeonhole and mutilated chessboard problems can be computed in polynomial time. As a byproduct we find a new `sandwich\" theorem that is similar to the sandwich theorem for Lovász' ϑ-function. Furthermore, the semidefinite relaxation gives a certificate of (un)satisfiability for 2SAT problems in polynomial time. By adding an objective function to the dual formulation, a specific class of polynomially solvable 3SAT instances can be identified. We conclude with discussing how the relaxation can be used to solve more general SAT problems and with some empirical observations.[PUBLICATION ABSTRACT]