1,118 research outputs found
The Forward-Backward-Forward Method from continuous and discrete perspective for pseudo-monotone variational inequalities in Hilbert spaces
Tseng's forward-backward-forward algorithm is a valuable alternative for
Korpelevich's extragradient method when solving variational inequalities over a
convex and closed set governed by monotone and Lipschitz continuous operators,
as it requires in every step only one projection operation. However, it is
well-known that Korpelevich's method converges and can therefore be used also
for solving variational inequalities governed by pseudo-monotone and Lipschitz
continuous operators. In this paper, we first associate to a pseudo-monotone
variational inequality a forward-backward-forward dynamical system and carry
out an asymptotic analysis for the generated trajectories. The explicit time
discretization of this system results into Tseng's forward-backward-forward
algorithm with relaxation parameters, which we prove to converge also when it
is applied to pseudo-monotone variational inequalities. In addition, we show
that linear convergence is guaranteed under strong pseudo-monotonicity.
Numerical experiments are carried out for pseudo-monotone variational
inequalities over polyhedral sets and fractional programming problems
Solving Variational Inequalities with Monotone Operators on Domains Given by Linear Minimization Oracles
The standard algorithms for solving large-scale convex-concave saddle point
problems, or, more generally, variational inequalities with monotone operators,
are proximal type algorithms which at every iteration need to compute a
prox-mapping, that is, to minimize over problem's domain the sum of a
linear form and the specific convex distance-generating function underlying the
algorithms in question. Relative computational simplicity of prox-mappings,
which is the standard requirement when implementing proximal algorithms,
clearly implies the possibility to equip with a relatively computationally
cheap Linear Minimization Oracle (LMO) able to minimize over linear forms.
There are, however, important situations where a cheap LMO indeed is available,
but where no proximal setup with easy-to-compute prox-mappings is known. This
fact motivates our goal in this paper, which is to develop techniques for
solving variational inequalities with monotone operators on domains given by
Linear Minimization Oracles. The techniques we develope can be viewed as a
substantial extension of the proposed in [5] method of nonsmooth convex
minimization over an LMO-represented domain
A Douglas-Rachford splitting for semi-decentralized equilibrium seeking in generalized aggregative games
We address the generalized aggregative equilibrium seeking problem for
noncooperative agents playing average aggregative games with affine coupling
constraints. First, we use operator theory to characterize the generalized
aggregative equilibria of the game as the zeros of a monotone set-valued
operator. Then, we massage the Douglas-Rachford splitting to solve the monotone
inclusion problem and derive a single layer, semi-decentralized algorithm whose
global convergence is guaranteed under mild assumptions. The potential of the
proposed Douglas-Rachford algorithm is shown on a simplified resource
allocation game, where we observe faster convergence with respect to
forward-backward algorithms.Comment: arXiv admin note: text overlap with arXiv:1803.1044
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