General QOPs (quadratic optimization problems) have a linear objective function cTx to be maximized over a nonconvex compact feasible region F described by a finite number of quadratic inequalities. Difficulties in solving a QOP arise from the nonconvexity in its quadratic terms. We propose a branch and bound algorithm for QOPs where branching operations have been designed to effectively reduce...

Abstract: Training a SVR (support vector regression) requires the solution of a very large QP (quadratic programming) optimization problem. Despite the fact that this type of problem is well understood, the existing training algorithms are very complex and slow. In order to solve these problems, this paper firstly introduces a new way to make SVR have the similar mathematic form as that of a su...

Successive Quadratic Programming (SQP) has been the method of choice for the solution of many nonlinear programming problems in process engineering. However, for the solution of large problems with SQP based codes, the combinatorial complexity associated with active set quadratic programming (QP) methods can be a bottleneck in exploiting the problem structure. In this paper, we examine the meri...

this paper presents a fuzzy goal programming (fgp) methodology for solving bi-level quadratic programming (blqp) problems. in the fgp model formulation, firstly the objectives are transformed into fuzzy goals (membership functions) by means of assigning an aspiration level to each of them, and suitable membership function is defined for each objectives, and also the membership functions for vec...

Let F be a compact subset of the n-dimensional Euclidean space Rn represented by (finitely or infinitely many) quadratic inequalities. We propose two methods, one based on successive semidefinite programming (SDP) relaxations and the other on successive linear programming (LP) relaxations. Each of our methods generates a sequence of compact convex subsets Ck (k = 1, 2, . . . ) of Rn such that (...

‎here‎, ‎we give a two phases algorithm based on integrating differential evolution (de) algorithm with modified hybrid genetic algorithm (mhga) for solving the associated nonlinear programming problem of a nonlinear optimal control problem‎. ‎in the first phase‎, ‎de starts with a completely random initial population where each individual‎, ‎or solution‎, ‎is a random matrix of control input v...

multi objective quadratic fractional programming (moqfp) problem involves optimization of several objective functions in the form of a ratio of numerator and denominator functions which involve both contains linear and quadratic forms with the assumption that the set of feasible solutions is a convex polyhedral with a nite number of extreme points and the denominator part of each of the object...

First, an integer programming model is proposed to find an α-labeling for quadratic graphs. Then, a Tabu search algorithm is developed to solve large scale problems. The proposed approach can generate α-labeling for special classes of quadratic graphs, not previously reported in the literature. Then, the main theorem of the paper is presented. We show how a problem in graph theory c...

We formulate the NP-hard n-dimensional knapsack feasibility problem as an equivalent absolute value equation (AVE) in an n-dimensional noninteger real variable space and propose a finite succession of linear programs for solving the AVE. Exact solutions are obtained for 1880 out of 2000 randomly generated consecutive knapsack feasibility problems with dimensions between 500 and one million. For...