Learning during search allows solvers for discrete optimization problems to remember parts of the search that they have already performed and avoid revisiting redundant parts. Learning approaches pioneered by the SAT and CP communities have been successfully incorporated into the SCIP constraint integer programming platform. In this paper we show that performing a heuristic constraint programmi...

Judice and Pires developed in recent years principal pivoting methods for the solving of the so-called box linear complementarity problems (BLCPs) where the constraint matrices are restrictedly supposed to be of P–matrices. This paper aims at presenting a new principal pivoting scheme for BLCPs where the constraint matrices are loosely supposed to be row sufficient. This scheme can be applied t...

We consider the utility maximization problem for an investor who faces a solvency or risk constraint in addition to a budget constraint. The investor wishes to maximize her expected utility from terminal wealth subject to a bound on her expected solvency at maturity. We measure solvency using a solvency function applied to the terminal wealth. The motivation for our analysis is an optimal inves...

Matrix interpretations can be used to bound the derivational complexity of rewrite systems. We present a criterion that completely characterizes matrix interpretations that are polynomially bounded. It includes the method of upper triangular interpretations as a special case, and we prove that the inclusion is strict. The criterion can be expressed as a finite domain constraint system. It trans...

MINTO is a software system that solves mixed-integer linear programs by a branch-and-bound algorithm with linear programming relaxations. It also provides automatic constraint classification, preprocessing, primal heuristics and constraint generation. Moreover, the user can enrich the basic algorithm by providing a variety of specialized application routines that can customize MINTO to achieve ...

In this work we introduce a relaxed version of the constant positive linear dependence constraint qualification (CPLD) that we call RCPLD. This development is inspired by a recent generalization of the constant rank constraint qualification from Minchenko and Stakhovski that was called RCR. We show that RCPLD is enough to ensure the convergence of an augmented Lagrangian algorithm and asserts t...

We study estimation of the mean-covariance under joint constraint Σμ=μ for a multivariate normal. A reparametrized structured covariance is proposed through spectral decomposition Σ involving μ, reducing number parameters. approximate MLE by maximizing lower bound profile likelihood and follow similar strategy Bayesian estimation. The approximation performs best.

The SEQUENCE constraint is useful in modelling car sequencing, rostering, scheduling and related problems. We introduce half a dozen new encodings of the SEQUENCE constraint, some of which do not hinder propagation. We prove that, down a branch of a search tree, domain consistency can be enforced on the SEQUENCE constraint in just O(n log n) time. This improves upon the previous bound of O(n) f...

We propose a design methodology for explicit Model Predictive Control (MPC) that guarantees hard constraint satisfaction in the presence of finite precision arithmetic errors. The implementation of complex digital control techniques, like MPC, is becoming increasingly adopted in embedded systems, where reduced precision computation techniques are embraced to achieve fast execution and low power...

This paper presents a new constraint domain, where variables can be assigned values that are organised in a tree hierarchy. The introduction of this new constraint domain is motivated by applications for the configuration of product and services, for instance, in the context of e-commerce. The paper proposes a small constraint language, based on comparisons and the use of monotonic functions. A...