quadratic programming


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quadratic programming

[kwä′drad·ik ′prō‚gram·iŋ]
(mathematics)
A body of techniques developed to find extremal points for systems of quadratic inequalities.
References in periodicals archive ?
Its dual problem is similarly deduced as C - SVM to be a convex quadratic programming
This research axis occurs again platform between positive semi-definite quadratic programming and diophantine equations.
In this paper, we consider the binary quadratic programming and its corresponding reformulation of the SDP relaxation directly.
* The Quadratic Programming (QP) subproblems are always consistent, i.e., a feasible solution always exists, and
In Section 2 we describe the convex quadratic programming relaxation and approximation results for unrelated machine scheduling without release dates.
Problems associated with applying portfolio selection techniques ranging from the Markowitz Quadratic Programming Technique to the Smith Simple Arithmetic Algorithm are discussed.
The problem can be formulated as a zero-one quadratic programming problem as follows.
Sequential Quadratic Programming. SQP is an efficient method for nonlinear optimization with advantages of high computational efficiency and fast convergent rate.
Interval quadratic programming is a development of the classic quadratic programming that utilizes interval analysis theory developed by Moore [3].
Lee, "Interval regression analysis by quadratic programming approach," IEEE Transactions on Fuzzy Systems, vol.
Sequential Quadratic Programming. We opted to use the SQP algorithm as an approach for the synthesis of the TDTD antenna arrays for its good performance in solving constrained optimization problems with nonlinear multivariable functions.
The optimization problems in standard MPC algorithms are in general the linear programming (LP) or linear quadratic programming (QP), and the computation time for LP/QP can not be neglected when the controlled systems have fast dynamics.

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