Difference of convex functions optimization algorithms (DCA) for globally minimizing nonconvex quadratic forms on Euclidean balls and spheres

We present DCA for globally minimizing quadratic forms on Euclidean balls and spheres. Since these problems admit at most one local-nonglobal minimizer, DCA converges in general to a solution for these problems. Numerical simulations show robustness, stability and efficiency of DCA with respect to r...

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Veröffentlicht in:Operations research letters Jg. 19; H. 5; S. 207 - 216
Hauptverfasser: Tao, Pham Dinh, An, Le Thi Hoai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.11.1996
Elsevier
Schlagworte:
Applied sciences
Computer Science
d.c. optimization
d.c. optimization algorithms
Exact sciences and technology
Local and global optimality conditions
Mathematical programming
Nonconvex quadratic minimization
Operational research and scientific management
Operational research. Management science
Optimization. Search problems
d.c. optimization algorithms
Local and global optimality conditions
Nonconvex quadratic minimization
d.c. optimization
Non convex programming
Algorithms
Euclidean theory
Optimality condition
Spheres
Quadratic programming
Optimization
ISSN:0167-6377, 1872-7468
Online-Zugang:Volltext
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Zusammenfassung:We present DCA for globally minimizing quadratic forms on Euclidean balls and spheres. Since these problems admit at most one local-nonglobal minimizer, DCA converges in general to a solution for these problems. Numerical simulations show robustness, stability and efficiency of DCA with respect to related standard methods.
ISSN:0167-6377
1872-7468
DOI:10.1016/S0167-6377(96)00036-3