Strong Duality for General Quadratic Programs with Quadratic Equality ConstraintsQuadratic programmingStrong duality for nonconvex optimizationCopositive matricesIn this article, by 'general quadratic program' we mean an optimization problem, in which all functions involved are quadratic or linear and local ...
We propose a distributed algorithm for strictly convex quadratic programming (QP) problems with a generic coupling topology. The coupling constraints are d... A Kozma,JV Frasch,M Diehl - IEEE 被引量: 24发表: 2013年 Solution to nonconvex quadratic programming with both inequality and box constrai...
In other words, the convex quadratic problems produce a normal form for the local topological structure of stationary point sets. As a consequence we see, as far as no equality constraints are involved, that the closure of the stationary point set constitutes a manifold with boundary. The ...
The censored linear $l_1 $ approximation problem is to minimize the nonconvex piecewise linear function $F(x) = \\sum _{i = 1}^m |y_i - \\max (z_i ,x^T a_i )|$. The problem arises in regression models where the range of the dependent variable is restricted. Unlike the maxi...
This library provides a one-stop shopsolve_qpfunction to solve convex quadratic programs: minimizex12xTPx+qTxsubject toGx≤hAx=blb≤x≤ub Vector inequalities apply coordinate by coordinate. The function returns the primal solutionx∗found by the backend QP solver, orNonein case of failure/unfea...
In this paper,we give iterative methods for solvingthe equality constrained quadratic programming problem. 给出了等式约束二次规划问题和等式约束加权最小二乘问题的迭代解 更多例句>> 3) nonconvex QCQP with a single quadratic constraint 单二次约束非凸二次规划问题 ...
Since the quadratic function is convex, and the constraints are linear, it is easier to solve a QP problem than other types of NLP [116]. A general QP problem can be written as: (4)minimize12xTQx+cTx,subject toAx≤b, where x is the vector containing the optimization variables, Q is...
Standard quadratic programs can also be solved by finite branch-and-bound methods proposed for solving more general nonconvex quadratic programming problems (see, e.g., [12, 13]). These approaches are based on an implicit enumeration of the complementarity constraints in the KKT conditions. The ...
Computational methods are proposed for solving a convex quadratic program (QP). Active-set methods are defined for a particular primal and dual formulation of a QP with general equality constraints and simple lower bounds on the variables. In the first part of the paper, two methods are proposed...
We consider low-rank semidefinite programming (LRSDP) relaxations of ±1 quadratic problems that can be formulated as the nonconvex nonlinear programming problem of minimizing a quadratic function subject to separable quadratic equality constraints. We prove the equivalence of the LRSDP problem with the...