A new branch-and-cut algorithm for non-convex quadratic programming via alternative direction method and semidefinite relaxation
DOI10.1007/s11075-020-01065-7zbMath1489.65087OpenAlexW3167349343MaRDI QIDQ820743
Huixian Wu, Sikai Chen, Hezhi Luo
Publication date: 27 September 2021
Published in: Numerical Algorithms (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11075-020-01065-7
computational complexityquadratic programmingbranch-and-cut algorithmsemidefinite relaxationalternative direction method
Numerical mathematical programming methods (65K05) Semidefinite programming (90C22) Polyhedral combinatorics, branch-and-bound, branch-and-cut (90C57) Nonconvex programming, global optimization (90C26) Quadratic programming (90C20)
Related Items (2)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Representing quadratically constrained quadratic programs as generalized copositive programs
- An eigenvalue decomposition based branch-and-bound algorithm for nonconvex quadratic programming problems with convex quadratic constraints
- Solving a class of low rank d.c. programs via a branch and bound approach: a computational experience
- Convex relaxations for nonconvex quadratically constrained quadratic programming: matrix cone decomposition and polyhedral approximation
- Nonconvex quadratically constrained quadratic programming: Best D.C. Decompositions and their SDP representations
- Convex relaxations of non-convex mixed integer quadratically constrained programs: projected formulations
- Approximation algorithms for indefinite quadratic programming
- A branch and reduce approach for solving a class of low rank d.c. programs
- Globally solving box-constrained nonconvex quadratic programs with semidefinite-based finite branch-and-bound
- Semidefinite programming versus the reformulation-linearization technique for nonconvex quadratically constrained quadratic programming
- Checking local optimality in constrained quadratic programming is NP- hard
- Quadratic programming with one negative eigenvalue is NP-hard
- A branch and bound method via d. c. optimization algorithms and ellipsoidal technique for box constrained nonconvex quadratic problems
- A reformulation-linearization technique for solving discrete and continuous nonconvex problems
- New properties and computational improvement of the GOP algorithm for problems with quadratic objective functions and constraints
- Solving a class of linearly constrained indefinite quadratic problems by DC algorithms
- On the complexity of approximating a KKT point of quadratic programming
- Exact solutions of some nonconvex quadratic optimization problems via SDP and SOCP relaxa\-tions
- Semidefinite programming relaxations for semialgebraic problems
- Approximating quadratic programming with bound and quadratic constraints
- An efficient algorithm for globally minimizing a quadratic function under convex quadratic constraints
- Quadratic maximization and semidefinite relaxation
- Enhancing semidefinite relaxation for quadratically constrained quadratic programming via penalty methods
- New global algorithms for quadratic programming with a few negative eigenvalues based on alternative direction method and convex relaxation
- Globally solving nonconvex quadratic programming problems via completely positive programming
- A polyhedral study of nonconvex quadratic programs with box constraints
- A branch-and-cut algorithm for nonconvex quadratic programs with box constraints
- A finite algorithm for a particular D.C. quadratic programming problem
- Global algorithm for solving linear multiplicative programming problems
- A finite branch-and-bound algorithm for nonconvex quadratic programming via semidefinite relaxations
- Semi-definite programming relaxation of quadratic assignment problems based on nonredundant matrix splitting
- \(NP\)-hardness of linear multiplicative programming and related problems
- Computable representations for convex hulls of low-dimensional quadratic forms
- Convex relaxations of non-convex mixed integer quadratically constrained programs: Extended formulations
- A practicable branch-and-bound algorithm for globally solving linear multiplicative programming
- A cutting plane algorithm for solving bilinear programs
- Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
- Semidefinite relaxation and nonconvex quadratic optimization
- SDPT3 — A Matlab software package for semidefinite programming, Version 1.3
- Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
- Nonlinear Optimization by Successive Linear Programming
This page was built for publication: A new branch-and-cut algorithm for non-convex quadratic programming via alternative direction method and semidefinite relaxation