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Articles 92 registres trobats  inicianterior83 - 92  anar al registre: La cerca s'ha fet en 0.00 segons. 
83.
27 p, 1.1 MB A dual-active-set algorithm for positive semi-definite quadratic programming / Boland, N. L.
Because of the many important applications of quadratic programming, fast and efficient methods for solving quadratic programming problems are valued. Goldfarb and Idnani (1983) describe one such method. [...]
1997
Mathematical Programming, vol. 78 n. 1 (1997) p. 1-27  
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84.
17 p, 827.7 KB On piecewise quadratic Newton and trust region problems / Sun, J.
Some recent algorithms for nonsmooth optimization require solutions to certain piecewise quadratic programming subproblems. Two types of subproblems are considered in this paper. The first type seeks the minimization of a continuously differentiable and strictly convex piecewise quadratic function subject to linear equality constraints. [...]
1997
Mathematical Programming, vol. 76 n. 3 (1997) p. 451-467  
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85.
19 p, 845.2 KB A trust region method for minimization of nonsmooth functions with linear constraints / Martínez, José Mario ; Moretti, Antonio Carlos
We introduce a trust region algorithm for minimization of nonsmooth functions with linear constraints. At each iteration, the objective function is approximated by a model function that satisfies a set of assumptions stated recently by Qi and Sun in the context of unconstrained nonsmooth optimization. [...]
1997
Mathematical Programming, vol. 76 n. 3 (1997) p. 431-449  
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86.
19 p, 655.9 KB A preconditioning proximal Newton method for nondifferentiable convex optimization / Qi, Liqun ; Chen, Xiaojun
We propose a proximal Newton method for solving nondifferentiable convex optimization. This method combines the generalized Newton method with Rockafellar's proximal point algorithm. At each step, the proximal point is found approximately and the regularization matrix is preconditioned to overcome inexactness of this approximation. [...]
1997
Mathematical Programming, vol. 76 n. 3 (1997) p. 411-429  
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87.
18 p, 881.4 KB Variable metric bundle methods : From conceptual to implementable forms / Lemaréchal, Claude ; Sagastizábal, Claudia
To minimize a convex function, we combine Moreau-Yosida regularizations, quasi-Newton matrices and bundling mechanisms. First we develop conceptual forms using "reversal" quasi-Newton formulae and we state their global and local convergence. [...]
1997
Mathematical Programming, vol. 76 n. 3 (1997) p. 393-410  
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88.
19 p, 774.0 KB Separating plane algorithms for convex optimization / Nurminski, Evgeni A.
The equivalent formulation of a convex optimization problem is the computation of a value of a conjugate function at the origin. The latter can be achieved by approximation of the epigraph of the conjugate function around the origin and gradual refinement of the approximation. [...]
1997
Mathematical Programming, vol. 76 n. 3 (1997) p. 373-391  
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89.
20 p, 827.4 KB Constraint aggregation principle in convex optimization / Ermoliev, Yuri M. ; Kryazhimskii, Arkadii V. ; Ruszczynski, Andrzej
A general constraint aggregation technique is proposed for convex optimization problems. At each iteration a set of convex inequalities and linear equations is replaced by a single surrogate inequality formed as a linear combination of the original constraints. [...]
1997
Mathematical Programming, vol. 76 n. 3 (1997) p. 353-372  
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90.
25 p, 1.0 MB On the degree and separability of nonconvexity and applications to optimization problems / Thach, Phan Thien ; Konno, Hiroshi
We study qualitative indications for d. c. representations of closed sets in and functions on Hilbert spaces. The first indication is an index of nonconvexity which can be regarded as a measure for the degree of nonconvexity. [...]
1997
Mathematical Programming, vol. 77 n. 1 (1997) p. 23-47  
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91.
10 p, 451.8 KB A capacity scaling algorithm for convex cost submodular flows / Iwata, Satoru
This paper presents a scaling scheme for submodular functions. A small but strictly submodular function is added before scaling so that the resulting functions should be submodular. This scaling scheme leads to a weakly polynomial algorithm to solve minimum cost integral submodular flow problems with separable convex cost functions, provided that an oracle for exchange capacities is available. [...]
1997
Mathematical Programming, vol. 76 n. 2 (1997) p. 299-308  
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92.
20 p, 952.0 KB Nonlinear rescaling and proximal-like methods in convex optimization / Polyak, Roman ; Teboulle, Marc
The nonlinear rescaling principle (NRP) consists of transforming the objective function and/or the constraints of a given constrained optimization problem into another problem which is equivalent to the original one in the sense that their optimal set of solutions coincides. [...]
1997
Mathematical Programming, vol. 76 n. 2 (1997) p. 265-284  
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Articles : 92 registres trobats   inicianterior83 - 92  anar al registre:
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