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.

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Mathematical programming

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To enable fast and efficient computation, we formulate the optimization problem as a quadratic program. We also examine how the inclusion of cash management bills would affect costs, using a heuristic approach based on shadow prices derived from the quadratic programming solution.

THE EFFECT OF "REGULAR AND PREDICTABLE" ISSUANCE ON TREASURY BILL FINANCING

(12) Solve the quadratic program (linear program with quadratic objective function).

Towards Merging Binary Integer Programming Techniques with Genetic Algorithms

Huang, "A new class of interval projection neural networks for solving interval quadratic program," Chaos, Solitons and Fractals, vol.

Robust Linear Neural Network for Constrained Quadratic Optimization

After the transformation, the original problem was transformed into an equivalent convex quadratic program problem:

Incremental activation detection for real-time fMRI series using robust kalman filter

Recently, many modern heuristics stochastic search algorithms such as genetic algorithm (GA) [6], evolutionary programming (EP) [7], tabu search (TS) [8], particle swam optimization (PSO) [9, 10], differential evolution algorithm (DE) [11-21], biogeography-based optimization (BBO) [22], chaotic self-adaptive differential harmony search algorithm (CSADHS) [23], quadratically constrained quadratic program method (QCQP) [24], Krill herd algorithm (KHA) [25], and harmony search with new pitch (NPAHS) [26] have shown great potentials in solving the nonlinear ELD or DED problems.

An improved differential evolution method based on the dynamic search strategy to solve dynamic economic dispatch problem with valve-point effects

Solving a linear program is much easier and faster than solving a quadratic program. The optimal portfolio consists of at most 2T + 2 assets regardless of the size, n while Markowitz model may consist of n assets.

An empirical comparison of different risk measures in portfolio optimization

The previous optimization problem is a convex quadratic program which can be solved by using the well-known Lagrange multiplier method.

Artificial immune system for support vector machine model selection

Therefore, the problem can be formulated as an integer quadratic program in n [multiplied by] m assignment variables.

Convex Quadratic and Semidefinite Programming Relaxations in Scheduling

For an iterative point [x.sup.k], a new quadratic program (QP) subproblem is given by

A QP-free algorithm for finite minimax problems

Recently, a new but important linear conic tool called completely positive programming (CPP) has been used to study the nonconvex quadratic program with linear and binary constraints.

A New Conic Approach to Semisupervised Support Vector Machines

[33] formulated the GAP as a mixed binary quadratic program with minimizing the slack time overall variance as the objective function; an assumption has been stated such that the flights are sequenced with the smallest arrival time.