AbstractTheoretical results relating to the facial structure of the general binary integer-programming polytope conv({x ∈ {0, 1} : Ax ≤ b}) where A ∈ r n × R , and b ∈R are presented. A conflict hypergraph is constructed and some induced hyperstars create valid inequalities of . BIP P These inequalities are further shown to produce large dimensional faces. Some computational results show the b...

Methods for constructing binary exhaustive prefix codes with certain synchronizing properties are given. In particular it is shown how to formulate the requirement that a code be anagrammatic and the requirement for minimal synchronizing delay as linear integer programming problems. The tree structure of the code is given and the program optimizes over all possible labelings of the tree. Severa...

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...

Domain filtering local consistencies, such as inverse consistencies, that only delete values and do not add new constraints are particularly useful in Constraint Programming. Although many such consistencies for binary constraints have been proposed and evaluated, the situation with non-binary constraints is quite different. Only very recently have domain filtering consistencies stronger than G...

The problem of scheduling pumps is widely discussed in the literature context improving energy efficiency, production costs, emissions, and reliability. In some studies, authors analyze available case studies compare results; others present their own computational methods. paper, a pump regular everyday operations water supply operator considered. issues optimization savings are part topic sust...

The Binary Integer Programming problem (BIP) is a mathematical optimization problem, with linear objective function and constraints, on which the domain of all variables {0, 1}. In BIP, every variable associated determined cost coefficient. Minmax regret under interval uncertainty (M-BIP) generalization BIP in coefficient to not known advance, but are assumed be bounded by an interval. M-BIP fi...

This lecture provides an in-depth explanation of ellipsoid algorithm which was developed in 1979 by a Soviet mathematician L.G. Khachian. Ellipsoid algorithm is the first polynomial-time linear programming algorithm which also knows as Khachiyan’s algorithm to acknowledge Khachiyan’s discovery. This algorithm is fundamentally different from Simplex Algorithm in a sense that it does not exploit ...

We compare two heuristic approaches, evolutionary computation and ant colony optimisation, and a complete tree-search approach, constraint programming, for solving binary constraint satisfaction problems. We experimentally show that, if evolutionary computation is far from being able to compete with the two other approaches, ant colony optimisation nearly always succeeds in finding a solution, ...

Statistical techniques for designing and analysing experiments are used to evaluate the individual and combined effects of genetic programming parameters. Three binary classification problems are investigated in a total of seven experiments consisting of 1108 runs of a machine code genetic programming system. The parameters having the largest effect in these experiments are the population size ...