Displaying similar documents to “A Mixed Integer Quadratic Programming Model for the Low Autocorrelation Binary Sequence Problem”

Optimal lot size determination of multistage production system

Jindřich L. Klapka (1978)

Aplikace matematiky

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This paper deals with the optimization of total setup plus inventory cost of a certain class of the multistage inventory-production systems with the series arranged production stages having generally different production rates, separated by stores from each other. The optimization is made by the choice of lot sizes across an infinite time horizon. The exact cost-optimization algorithm based on the Bellman optimality principle is derived and applied for deriving two lower bounds of the...

Inequality-sum : a global constraint capturing the objective function

Jean-Charles Régin, Michel Rueher (2005)

RAIRO - Operations Research - Recherche Opérationnelle

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This paper introduces a new method to prune the domains of the variables in constrained optimization problems where the objective function is defined by a sum y = Σ x i , and where the integer variables x i are subject to difference constraints of the form x j - x i ≤ c . An important application area where such problems occur is deterministic scheduling with the mean flow time as optimality criteria. This new constraint is also more general than a sum constraint defined on a set of ordered variables. Classical...

Reformulations in Mathematical Programming: Definitions and Systematics

Leo Liberti (2009)

RAIRO - Operations Research

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A reformulation of a mathematical program is a formulation which shares some properties with, but is in some sense better than, the original program. Reformulations are important with respect to the choice and efficiency of the solution algorithms; furthermore, it is desirable that reformulations can be carried out automatically. Reformulation techniques are widespread in mathematical programming but interestingly they have never been studied under a unified framework. This paper attempts...

A nonsmooth version of the univariate optimization algorithm for locating the nearest extremum (locating extremum in nonsmooth univariate optimization)

Marek Smietanski (2008)

Open Mathematics

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An algorithm for univariate optimization using a linear lower bounding function is extended to a nonsmooth case by using the generalized gradient instead of the derivative. A convergence theorem is proved under the condition of semismoothness. This approach gives a globally superlinear convergence of algorithm, which is a generalized Newton-type method.