The Experts below are selected from a list of 270 Experts worldwide ranked by ideXlab platform
N. Grudinin - One of the best experts on this subject based on the ideXlab platform.
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Reactive power optimization using successive Quadratic Programming method
IEEE Transactions on Power Systems, 1998Co-Authors: N. GrudininAbstract:This paper presents a reactive power optimization model that is based on successive Quadratic Programming (SQP) methods. Mathematical formulation and unified algorithm suppose different objective functions (OF) of reactive power optimization, depending on type and purposes of current reactive power control or planning problem. A bicriterion reactive power optimization model, that represents compromise between economical and security objective functions, is proposed. An efficient algorithm for approximation of initial problem by Quadratic Programming problem is described. The Quadratic Programming problem (QP) is solved on the basis of the Newton type Quadratic Programming method. A modified successive Quadratic Programming method was developed, that provides reliable convergence of the SQP method.
D Leedaniel - One of the best experts on this subject based on the ideXlab platform.
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Multiplicative Updates for Nonnegative Quadratic Programming
Neural Computation, 2007Co-Authors: Shafei, Linyuanqing, K Saullawrence, D LeedanielAbstract:Many problems in neural computation and statistical learning involve optimizations with nonnegativity constraints. In this article, we study convex problems in Quadratic Programming where the optim...
José L. Verdegay - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy costs in Quadratic Programming problems
Fuzzy Optimization and Decision Making, 2013Co-Authors: Ricardo C. Silva, Carlos Cruz, José L. VerdegayAbstract:Although Quadratic Programming problems are a special class of nonlinear Programming, they can also be seen as general linear Programming problems. These Quadratic problems are of the utmost importance in an increasing variety of practical fields. As, in addition, ambiguity and vagueness are natural and ever-present in real-life situations requiring operative solutions, it makes perfect sense to address them using fuzzy concepts formulated as Quadratic Programming problems with uncertainty, i.e., as Fuzzy Quadratic Programming problems. This work proposes two novel fuzzy-sets-based methods to solve a particular class of Fuzzy Quadratic Programming problems which have vagueness coefficients in the objective function. Moreover, two other linear approaches are extended to solve the Quadratic case. Finally, it is shown that the solutions reached from the extended approaches may be obtained from two proposed parametric multiobjective approaches.
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Strict sensitivity analysis in fuzzy Quadratic Programming
Fuzzy Sets and Systems, 2012Co-Authors: Behrouz Kheirfam, José L. VerdegayAbstract:Quadratic Programming can be seen both as a general approach to linear Programming and a special class of non-linear Programming. Moreover, Quadratic Programming problems are of utmost importance in an increasing variety of practical fields, such as, regression, efficient production and portfolio selection. As ambiguity and vagueness are natural and ever-present in real-life situations requiring solutions, it makes perfect sense to attempt to address them using fuzzy Quadratic Programming problems. The main purpose of this paper is to study the strictly sensitivity analysis for fuzzy Quadratic Programming when simultaneously and independently variations occur in the right-hand-side of the constraints and the coefficients of the objective function. One presents computable auxiliary problems to identify the invariance intervals and give a fuzzy Quadratic form of the optimal value function too. Some numerical examples are presented to illustrate the proposed method.
Shafei - One of the best experts on this subject based on the ideXlab platform.
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Multiplicative Updates for Nonnegative Quadratic Programming
Neural Computation, 2007Co-Authors: Shafei, Linyuanqing, K Saullawrence, D LeedanielAbstract:Many problems in neural computation and statistical learning involve optimizations with nonnegativity constraints. In this article, we study convex problems in Quadratic Programming where the optim...
Angelo Lucia - One of the best experts on this subject based on the ideXlab platform.
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Methods of successive Quadratic Programming
Computers & Chemical Engineering, 1994Co-Authors: Angelo LuciaAbstract:Abstract The occurrence of nondescent directions in successive Quadratic Programming is studied. It is shown that simple chemical process examples can be constructed that exhibit nondescent as a consequence of the projected indefiniteness of the Hessian matrix of the Lagrangian function. Moreover, in situations where multiple Kuhn-Tucker points for the Quadratic Programming sub-problems exist, the global optimum need not necessary provide a direction of descent. Thus search for a global solution is unjustified. To circumvent these difficulties, a linear Programming-based trust region method is proposed to guarantee descent for any arbitrary merit function, provided such a direction exists. Geometric illustrations are used to elucidate the main ideas.
