Zobrazeno 11 - 20
of 292
pro vyhledávání: '"Combettes, Patrick L."'
We introduce a framework based on Rockafellar's perturbation theory to analyze and solve general nonsmooth convex minimization and monotone inclusion problems involving nonlinearly composed functions as well as linear compositions. Such problems have
Externí odkaz:
http://arxiv.org/abs/2111.11421
Autor:
Bùi, Minh N., Combettes, Patrick L.
We investigate a modular convex Nash equilibrium problem involving nonsmooth functions acting on linear mixtures of strategies, as well as smooth coupling functions. An asynchronous block-iterative decomposition method is proposed to solve it.
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Externí odkaz:
http://arxiv.org/abs/2111.01781
A Variational Inequality Model for the Construction of Signals from Inconsistent Nonlinear Equations
Building up on classical linear formulations, we posit that a broad class of problems in signal synthesis and in signal recovery are reducible to the basic task of finding a point in a closed convex subset of a Hilbert space that satisfies a number o
Externí odkaz:
http://arxiv.org/abs/2105.07380
Under consideration are multicomponent minimization problems involving a separable nonsmooth convex function penalizing the components individually, and nonsmooth convex coupling terms penalizing linear mixtures of the components. We investigate bloc
Externí odkaz:
http://arxiv.org/abs/2103.00520
Under investigation is the problem of finding the best approximation of a function in a Hilbert space subject to convex constraints and prescribed nonlinear transformations. We show that in many instances these prescriptions can be represented using
Externí odkaz:
http://arxiv.org/abs/2101.04074
Autor:
Bùi, Minh N., Combettes, Patrick L.
We propose an asynchronous block-iterative decomposition algorithm to solve Nash equilibrium problems involving a mix of nonsmooth and smooth functions acting on linear mixtures of strategies. The methodology relies heavily on monotone operator theor
Externí odkaz:
http://arxiv.org/abs/2101.00532
We introduce c-lasso, a Python package that enables sparse and robust linear regression and classification with linear equality constraints. The underlying statistical forward model is assumed to be of the following form: \[ y = X \beta + \sigma \eps
Externí odkaz:
http://arxiv.org/abs/2011.00898
The goal of this paper is to promote the use of fixed point strategies in data science by showing that they provide a simplifying and unifying framework to model, analyze, and solve a great variety of problems. They are seen to constitute a natural e
Externí odkaz:
http://arxiv.org/abs/2008.02260
Various strategies are available to construct iteratively a common fixed point of nonexpansive operators by activating only a block of operators at each iteration. In the more challenging class of composite fixed point problems involving operators th
Externí odkaz:
http://arxiv.org/abs/2005.10235
We consider the problem of recovering a signal from nonlinear transformations, under convex constraints modeling a priori information. Standard feasibility and optimization methods are ill-suited to tackle this problem due to the nonlinearities. We s
Externí odkaz:
http://arxiv.org/abs/2003.01260