Pages that link to "Item:Q598585"

The following pages link to Branch-and-bound approaches to standard quadratic optimization problems (Q598585):

Displaying 17 items.

• Think co(mpletely)positive! Matrix properties, examples and a clustered bibliography on copositive optimization (Q427378) (← links)
• Nonconvex quadratically constrained quadratic programming: Best D.C. Decompositions and their SDP representations (Q645557) (← links)
• New and old bounds for standard quadratic optimization: dominance, equivalence and incomparability (Q930342) (← links)
• A computational comparison of some branch and bound methods for indefinite quadratic programs (Q940829) (← links)
• Enhancing semidefinite relaxation for quadratically constrained quadratic programming via penalty methods (Q1730782) (← links)
• On convex relaxations for quadratically constrained quadratic programming (Q1925792) (← links)
• A study on sequential minimal optimization methods for standard quadratic problems (Q2095525) (← links)
• Global solutions of nonconvex standard quadratic programs via mixed integer linear programming reformulations (Q2231320) (← links)
• D.C. versus copositive bounds for standard QP (Q2583183) (← links)
• Computable representations for convex hulls of low-dimensional quadratic forms (Q2638365) (← links)
• (Q2941669) (← links)
• Dynamically generated cutting planes for mixed-integer quadratically constrained quadratic programs and their incorporation into GloMIQO 2 (Q2943816) (← links)
• The fundamental theorem of linear programming: extensions and applications (Q2996803) (← links)
• A new branch-and-bound algorithm for standard quadratic programming problems (Q4646674) (← links)
• Some remarks on duality and optimality of a class of constrained convex quadratic minimization problems (Q4987691) (← links)
• Outcome-space branch-and-bound outer approximation algorithm for a class of non-convex quadratic programming problems (Q6102175) (← links)
• Performance comparison of two recently proposed copositivity tests (Q6114943) (← links)