Quadratic programming


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Quadratic programming

Variant of linear programming in which the objective function is quadratic rather than linear. In portfolio selection, we often minimize the variance of the portfolio (which is a quadratic function) subject to constraints on the mean return of the portfolio.
References in periodicals archive ?
The proposed coding scheme for the solution of economic dispatch using Quadratic Programming using Wolfe's Method has been tested on the six unit system and the numerical result obtained through the MATLAB simulation.
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.
In fact, introducing the transformation x = [PHI}(x) and the corresponding kernel K(x, x') = ([PHI](x) x [PHI](x')), the primal problem becomes the convex quadratic programming
Hence dual bounds of quadratic programming arise in resolution techniques of nonlinear optimization problems.
In this paper, we consider the following binary quadratic programming
Adapa, "A Review of Selected Optimal Power Flow Literature to 1993 Part I: Nonlinear and Quadratic Programming Approaches", IEEE Transaction on Power Systems, vol.
The most promising results are obtained when special-purpose sequential quadratic programming (SQP) algorithms are embedded into stochastic global algorithms.
In addition to the variance, both beta and LPM statistics can be formulated and used in quadratic programming analysis.
Individual investors who are faced with portfolio selection decisions may choose from diversification techniques which range from the most simple and naive forms to more highly complex schemes requiring quadratic programming.
Computational results are reported, and suggestions are given for future work on simulated annealing heuristics for quadratic programming problems.