In this paper, a subspace limited memory BFGS algorithm for solving large-scale bound constrained optimization problems is developed. It is modifications of the subspace limited memory quasi-Newton method proposed by Ni and Yuan [Q. Ni, Y.X. Yuan, A subspace limited memory quasi-Newton algorithm for
β¦ LIBER β¦
Randomized algorithm for global optimization with bounded memory
β Scribed by James M. Calvin
- Publisher
- Elsevier Science
- Year
- 2010
- Tongue
- English
- Weight
- 167 KB
- Volume
- 80
- Category
- Article
- ISSN
- 0378-4754
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Modified subspace limited memory BFGS al
β
Yunhai Xiao; Hongchuan Zhang
π
Article
π
2008
π
Elsevier Science
π
English
β 516 KB
An interval algorithm for constrained gl
β
M.A. Wolfe
π
Article
π
1994
π
Elsevier Science
π
English
β 589 KB
A multi dynamics algorithm for global op
β
J.A. HernΓ‘ndez; J.D. Ospina
π
Article
π
2010
π
Elsevier Science
π
English
β 532 KB
Parallel global optimization with the pa
β
J. F. Schutte; J. A. Reinbolt; B. J. Fregly; R. T. Haftka; A. D. George
π
Article
π
2004
π
John Wiley and Sons
π
English
β 291 KB
Randomized optimal algorithm for slope s
β
JiΕΓ MatouΕ‘ek
π
Article
π
1991
π
Elsevier Science
π
English
β 689 KB
Tabu search method with random moves for
β
Nanfang Hu
π
Article
π
1992
π
John Wiley and Sons
π
English
β 601 KB
Optimum engineering design problems are usually formulated as non-convex optimization problems of continuous variables. Because of the absence of convexity structure, they can have multiple minima, and global optimization becomes difficult. Traditional methods of optimization, such as penalty method