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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.
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It has become one of the popular methods in machine learning because of its low computational complexity, since it solves above two smaller sized convex quadratic programming problems.
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.
Hence dual bounds of quadratic programming arise in resolution techniques of nonlinear optimization problems.
Keywords Binary quadratic programming, successive quadratic programming algorithm, semidefinite programming, randomized method.
Adapa, "A Review of Selected Optimal Power Flow Literature to 1993 Part I: Nonlinear and Quadratic Programming Approaches", IEEE Transaction on Power Systems, vol.
This can be a big problem for sequential quadratic programming (SQP) based algorithms, generally considered the most successful algorithms available for NLP problems with a reasonable number of variables.
For the problem of scheduling unrelated parallel machines in the absence of nontrivial release dates R [parallel] [Sigma] [w.sub.j][C.sub.j], we introduce a convex quadratic programming relaxation that leads to a simple 3/2-approximation algorithm.
Using the above formulas, quadratic programming is set up to maximize return and minimize variance as follows:
(1) The quadratic programming solution procedure requires a considerable amount of computer time and space.
The first stage of the procedure solves the 0-1 quadratic programming model.
Computational results are reported, and suggestions are given for future work on simulated annealing heuristics for quadratic programming problems.
Four nonlinear optimization algorithms with constraints, quasi-newton Lagrangian multiplier method (QNLM), sequential quadratic programming (SQP) [14,15] adaptive genetic algorithms (AGA) [16,17], and particle swarm optimization with random weighting and natural selection (PSO-RN) [18, 19], are introduced to solve the objective function.

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