D.P. Bertsekas and P. Tseng, "Partial proximal minimization algorithms for convex programming", SIAM Journal on Optimization 4 (1994) 551--572. Home/Search Document Details and Download
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New Error Bounds and Their Applications to Convergence Analysis of.. - Luo (1999) (Correct)
.... the Goldstein Levitin Polyak gradient projection algorithm for convex minimization problems [9, 28] and an extension to monotone variational inequalities [3, 16] the Martinet Rockafellar proximal algorithms for convex minimization and setvalued inclusion [12, 17, 20, 28, 32] and its extensions [15, 4]; coordinate descent methods and the related dual relaxation methods [14, 26, 30] matrix splitting methods for symmetric, monotone linear complementarity problems and variational inequalities [27, 19] and asynchronous versions of some of these methods [31, 39] Several papers have presented a ....
D.P. Bertsekas and P. Tseng, "Partial proximal minimization algorithms for convex programming", SIAM Journal on Optimization 4 (1994) 551--572.
Cost Approximation Algorithms - Patriksson (Correct)
....If both f 1 and f 2 are nonzero, choosing t = 0 defines a partial linearization ( 25] of the original objective, wherein only f 2 is linearized. Letting x = x T 1 ; x T 2 ) T , the choice t (y) 1= 2fl t )ky 1 Gamma x t 1 k 2 leads to the partial proximal point algorithm ([20, 7]) choosing t (y) f(y 1 ; x t 2 ) leads to a linearization of f in the variables x 2 . Several well known methods can be derived either directly as CA algorithms, or as inexact proximal point algorithms. For example, the Levenberg Marquardt algorithm ( 49, 5] which is a Newton like ....
Bertsekas, D. P., and Tseng, P.: `Partial proximal minimization algorithms for convex programming ', SIAM Journal on Optimization 4 (1994), 551--572.
Proximal Minimization Methods with Generalized Bregman Functions - Kiwiel (1995) (21 citations) (Correct)
....Euclidean norm and fc k g is a sequence of positive numbers. The convergence and applications of the PPA are discussed, e.g. in [Aus86, CoL93, EcB92, GoT89, Gul91, Lem89, Roc76a, Roc76b] Several proposals have been made for replacing the quadratic term in (1. 2) with other distance like functions [BeT94, CeZ92, ChT93, Eck93, Egg90, Ius95, IuT93, Teb92, TsB93]. In [CeZ92] 1.2) is replaced by x k 1 = arg minf f(x) D h (x; x k ) c k : x 2 X g; 1:3) Research supported by the State Committee for Scientific Research under Grant 8S50502206. Systems Research Institute, Newelska 6, 01 447 Warsaw, Poland (kiwiel ibspan.waw.pl) where D h (x; y) ....
D. P. Bertsekas and P. Tseng, Partial proximal minimization algorithms for convex programming, SIAM J. Optim. 4 (1994) 551--572.
Parallel Nonlinear Optimisation for Decision Making Under .. - Chattratichat Darlington (Correct)
.... these gradients via finite difference perturbations [4] For systems with a small number of nonlinear constraints, linear algebraic strategies such as Jacobi and Guass Seidel iterations can be employed [3] A powerful algorithm however is the parallel proximal optimization approach (see, e.g. [1]) We have implementations of all these strategies within the robust optimization framework described above. The parallelization strategy chosen will of course depend on the problem at hand. The Jacobi, Guass Seidel and parallel proximal optimization approaches are suitable for systems with a ....
D.P. Bertsekas and P. Tseng "Partial Proximal Minimization Algorithms for Convex Programming", SIAM J. Optimization, vol. 4, pp. 551-572, 1994.
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