We describe a method for transforming beam search into a complete search algorithm that is guaranteed to find an optimal solution. Called beam-stack search, the algorithm uses a new data structure, called a beam stack, that makes it possible to integrate systematic backtracking with beam search. The resulting search algorithm is an anytime algorithm that finds a good, sub-optimal solution quick...

We propose a new backtracking algorithm called constraint-directed backtracking (CDBT) for solving general constraint-satisfaction problems (CSPs). CDBT and the naive backtracking (BT) share a similar style of instantiating and re-instantiating variables. They diier in that CDBT searches for an assignment to variables in a variable set from the given constraint posed on that variable set and ap...

We propose a new backtracking method called constraint-directed backtracking (CDBT) for solving constraint-satisfaction problems (CSPs). CDBT and chronological back-tracking (BT) share a similar style of instantiating variables (forward) and re-instantiating variables (backward). They diier in that CDBT searches instantiations of variables in a variable set from a given constraint posed on that...

We present Value Elimination, a new algorithm for Bayesian Inference. Given the same variable order­ ing information, Value Elimination can achieve per­ formance that is within a constant factor of variable elimination or recursive conditioning, and on some problems it can perform exponentially better, irrespec­ tive of the variable ordering used by these algorithms. Value Elimination's other f...

We present Value Elimination, a new algorithm for Bayesian Inference. Given the same variable ordering information, Value Elimination can achieve performance that is within a constant factor of variable elimination or recursive conditioning, and on some problems it can perform exponentially better, irrespective of the variable ordering used by these algorithms. Value Elimination’s other feature...

Multi-Point Constructive Search (MPCS) is a constructive search technique which borrows the idea from local search of being guided by multiple viewpoints. MPCS consists of a series of resource-limited backtracking searches: each starting from an empty solution or guided by one of a set of high quality, “elite” solutions encountered earlier in the search. This paper focuses on MPCS as applied to...

An extension of the Gauss{Newton method for nonlinear equations to convex composite optimization is described and analyzed. Local quadratic convergence is established for the minimization of h F under two conditions, namely h has a set of weak sharp minima, C, and there is a regular point of the inclusion F(x) 2 C. This result extends a similar convergence result due to Womersley which employs ...

The simple assembly line balancing problem (SALBP) is a well-studied NP-complete problem for which a new problem database of generated instances was published in 2013. This paper describes the application of a branch, bound, and remember (BB&R) algorithm using the cyclic best-first search strategy to this new database to produce provably exact solutions for 86% of the unsolved problems in this ...

Most CSP algorithms are based on refinements and extensions of backtracking, and employ one of two simple “branching schemes”: 2-way branching or d-way branching, for domain size d. The schemes are not equivalent, but little is known about their relative power. Here we compare them in terms of how efficiently they can refute an unsatisfiable instance with optimal branching choices, by studying ...

Almost ten years ago, Ralf Hinze has written a functional pearl on how to derive backtracking functionality for the purely functional programming language Haskell. In these notes, we show how to arrive at the efficient, two-continuation based backtracking monad derived by Hinze starting from an intuitive inefficient implementation that we subsequently refine using well known program transformat...