Integer programming


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Integer programming

Variant of linear programming in which the solution values must be integers.

Integer Programming

In mathematics, a process or technique for finding the maximum or minimum value of a linear function subject where the solution must be an integer. It is a form of linear programming, which is important to securities analysis as it helps determine the maximum or minimum rate of return on a particular investment. While the maximum rate of return is not necessarily the expected rate of return, integer programming may help an investor decide if a security is worth a certain level of risk.
References in periodicals archive ?
Reviving Integer Programming Approaches for AI Planning: A Branch-and-Cut Framework.
Gunasingh and Lashkari (1989) presented a 0/1 integer programming model with two alternative objective functions: 1) to maximize similarity between machines and parts or 2) to minimize the cost of machines less savings in intercellular movements.
Robert Causey provided extensive critiques of the solutions produced by the integer programming model.
Mixed Integer Programming by a Branch and Bound Technique.
Khorasaninejad, "Size optimization for hybrid photovoltaic-wind energy system using ant colony optimization for continuous domains based integer programming," Applied Soft Computing, vol.
In Section 2, the proposed problem is formulated as an integer programming problem by use of a decomposition based solution strategy that creates a master problem (a binary integer programming problem) and also a subproblem (column generation problem) to generate columns for the master problem.
of North Carolina at Wilmington) presents a varied selection of papers dealing with theory and applications in integer programming.
He covers linear programming formulations (allocation, covering, blending and network models and data envelopment analysis), sensitivity analysis in linear programs, integer programming, nonlinear programming, and heuristic solutions with the evolutionary solver, and includes case studies, exercises and appendices on software, graphical methods in linear programming, the simplex method, and stochastic programming.
Watson Research Center) discusses linear and integer programming, polytopes, matroids and matroid optimization, shortest paths, and network flows.