quadratic programming


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quadratic programming

[kwä′drad·ik ′prō‚gram·iŋ]
(mathematics)
A body of techniques developed to find extremal points for systems of quadratic inequalities.
References in periodicals archive ?
A primal-dual method for quadratic programming with bounded variables", Numerical Methods for Nonlinear Optimization, Academic Press, London, 1972, pp.
From this study we can conclude that one can gain the unique and best optimal solution by using the convex optimization approach in quadratic programming model.
Once the MPC is built under the constraints, it leads to a quadratic programming problem.
Lent and Censor (1980) presented an iterative relaxation procedure for the problem (27) which is an extension of Hildreth's quadratic programming algorithm, specially designed for approximating the minimum-norm element of a polyhedral convex set.
Convert the objective function of quadratic programming described as equation (4-7) to minimum; we can get equation (4-3).
Therefore, in the following analysis, only the key aspects of the quadratic programming models/revenue-sharing contract model and their results are introduced.
C - SVM formulates the problem as a convex quadratic programming
One convenient statistic that may be utilized to shortcut the complexities of the underlying quadratic programming problem and drive to the heart of the structure of the correlation of fatalities generated by various attack methods is a statistic that shall be called Beta.
i] can be found by solving the following convex quadratic programming problem:
A large number of these problems can be formulated as quadratic programming problems (QP) with a quadratic objective function and a linear set of equality or inequality constraints.
Abstract In this paper, we obtain a successive linear programming algorithm for solving the semidefinite programming (SDP) relaxation of the binary quadratic programming.