Using a known result on minimization of convex functionals on polyhedral cones, the Frank–Wolfe theorem, and basic linear algebra, we give a simple proof that the general convex quadratic programming problem which satisfies a natural necessary condition has a solution
The fundamental theorem of linear programming (LP) states that every feasible linear program that is...
The problem of minimizing a quadratic objective function subject to one or two quadratic constraints...
This paper describes a method of minimizing a strictly convex quadratic functional of several variab...
In this paper we consider the problem of minimizing a (possibly nonconvex) quadratic function with a...
AbstractA readily implementable algorithm is proposed for minimizing any convex, not necessarily dif...
Abstract In this paper we consider optimization problems dened by a quadratic objective function an...
Abstract. The famous Frank–Wolfe theorem ensures attainability of the op-timal value for quadratic o...
A solution procedure for linear programs with one convex quadratic constraint is suggested. The meth...
Nonconvex quadratic programming with box constraints is a fundamental NP-hard global optimization pr...
AbstractA convex quadratic program has Kuhn-Tucker conditions which are necessary and sufficient, an...
The nonconvex problem of minimizing the product of a strictly convex quadratic function and the p-th...
Problems of minimizing a convex function or maximizing a concave function over a convex set are call...
Nonconvex quadratic programming with box constraints is a fundamental NP-hard global optimization pr...
This paper gives several characterizations of the solution set of convex programs. No differentiabi...
Given a convex program with C2 functions and a convex set S of solutions to the problem, we give a s...
The fundamental theorem of linear programming (LP) states that every feasible linear program that is...
The problem of minimizing a quadratic objective function subject to one or two quadratic constraints...
This paper describes a method of minimizing a strictly convex quadratic functional of several variab...
In this paper we consider the problem of minimizing a (possibly nonconvex) quadratic function with a...
AbstractA readily implementable algorithm is proposed for minimizing any convex, not necessarily dif...
Abstract In this paper we consider optimization problems dened by a quadratic objective function an...
Abstract. The famous Frank–Wolfe theorem ensures attainability of the op-timal value for quadratic o...
A solution procedure for linear programs with one convex quadratic constraint is suggested. The meth...
Nonconvex quadratic programming with box constraints is a fundamental NP-hard global optimization pr...
AbstractA convex quadratic program has Kuhn-Tucker conditions which are necessary and sufficient, an...
The nonconvex problem of minimizing the product of a strictly convex quadratic function and the p-th...
Problems of minimizing a convex function or maximizing a concave function over a convex set are call...
Nonconvex quadratic programming with box constraints is a fundamental NP-hard global optimization pr...
This paper gives several characterizations of the solution set of convex programs. No differentiabi...
Given a convex program with C2 functions and a convex set S of solutions to the problem, we give a s...
The fundamental theorem of linear programming (LP) states that every feasible linear program that is...
The problem of minimizing a quadratic objective function subject to one or two quadratic constraints...
This paper describes a method of minimizing a strictly convex quadratic functional of several variab...
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