Results 51 to 60 of about 151 (140)
On the asymptotic behavior of the Douglas-Rachford and proximal-point algorithms for convex optimization. [PDF]
Optim Lett, 2021 Banjac G, Lygeros J.
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New duality results for evenly convex optimization problems. [PDF]
Optimization, 2021 Fajardo MD, Grad SM, Vidal J.
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Equilibria With Infinitely Many Differentiated Classes Of Customers
, 1997 . In this work we consider a bicriterion extension of equilibrium problems formulated as variational inequalities, and propose for its solution a generalization of the Frank-Wolfe method.
P. Marcotte, D. L. Zhu
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Minimizing Uniformly Convex Functions by Cubic Regularization of Newton Method. [PDF]
J Optim Theory Appl, 2021 Doikov N, Nesterov Y.
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Local convergence of tensor methods. [PDF]
Math Program, 2022 Doikov N, Nesterov Y.
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A Newton-like Method for Nonlinear Semidefinite Inequalities
, 2007 A matrix map F (x) is said to be (matricially) convex, if u T F (x)u is a convex function for every u. In this paper, semidefinite systems of the type F (x) ¯ 0, where F (x) is matricially convex, are considered. This class of problems generalizes both
Motakuri Ramana, A. J. Goldman
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STOCHASTIC OPTIMIZATION OVER A PARETO SET ASSOCIATED WITH A STOCHASTIC MULTI-OBJECTIVE OPTIMIZATION PROBLEM [PDF]
, 2020 . We deal with the problem of minimizing the expectation of a real valued random function over the weakly Pareto or Pareto set associated with a Stochastic MultiObjective Optimization Problem (SMOP) whose objectives are expectations of random functions ...
Julien Collonge, Henri Bonnel
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Trust Region Affine Scaling Algorithms for Linearly Constrained Convex and Concave Programs
, 1996 We study a trust region affine scaling algorithm for solving the linearly constrained convex or concave programming problem. Under primal nondegeneracy assumption, we prove that every accumulation point of the sequence generated by the algorithm ...
Yanhui Wang, Renato D.C. Monteiro
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Convex analysis on the Hermitian matrices
, 1996 There is growing interest in optimization problems with real symmetric matrices as variables. Generally the matrix functions involved are spectral: they depend only on the eigenvalues of the matrix.
A. S. Lewis
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