14,739 research outputs found
Branch-and-Prune Search Strategies for Numerical Constraint Solving
When solving numerical constraints such as nonlinear equations and
inequalities, solvers often exploit pruning techniques, which remove redundant
value combinations from the domains of variables, at pruning steps. To find the
complete solution set, most of these solvers alternate the pruning steps with
branching steps, which split each problem into subproblems. This forms the
so-called branch-and-prune framework, well known among the approaches for
solving numerical constraints. The basic branch-and-prune search strategy that
uses domain bisections in place of the branching steps is called the bisection
search. In general, the bisection search works well in case (i) the solutions
are isolated, but it can be improved further in case (ii) there are continuums
of solutions (this often occurs when inequalities are involved). In this paper,
we propose a new branch-and-prune search strategy along with several variants,
which not only allow yielding better branching decisions in the latter case,
but also work as well as the bisection search does in the former case. These
new search algorithms enable us to employ various pruning techniques in the
construction of inner and outer approximations of the solution set. Our
experiments show that these algorithms speed up the solving process often by
one order of magnitude or more when solving problems with continuums of
solutions, while keeping the same performance as the bisection search when the
solutions are isolated.Comment: 43 pages, 11 figure
Efficient Solving of Quantified Inequality Constraints over the Real Numbers
Let a quantified inequality constraint over the reals be a formula in the
first-order predicate language over the structure of the real numbers, where
the allowed predicate symbols are and . Solving such constraints is
an undecidable problem when allowing function symbols such or . In
the paper we give an algorithm that terminates with a solution for all, except
for very special, pathological inputs. We ensure the practical efficiency of
this algorithm by employing constraint programming techniques
Approximating a Wavefunction as an Unconstrained Sum of Slater Determinants
The wavefunction for the multiparticle Schr\"odinger equation is a function
of many variables and satisfies an antisymmetry condition, so it is natural to
approximate it as a sum of Slater determinants. Many current methods do so, but
they impose additional structural constraints on the determinants, such as
orthogonality between orbitals or an excitation pattern. We present a method
without any such constraints, by which we hope to obtain much more efficient
expansions, and insight into the inherent structure of the wavefunction. We use
an integral formulation of the problem, a Green's function iteration, and a
fitting procedure based on the computational paradigm of separated
representations. The core procedure is the construction and solution of a
matrix-integral system derived from antisymmetric inner products involving the
potential operators. We show how to construct and solve this system with
computational complexity competitive with current methods.Comment: 30 page
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