AbstractA randomized strategy or a convex combination may be represented by a probability vector p = (p1, …, pm). p is called sparse if it has few positive entries. This paper presents an approximation lemma and applies it to matrix games, linear programming, computer chess, and uniform sampling spaces. In all cases arbitrary probability vectors can be replaced by sparse ones (with only logarithmically many positive entries) without losing too much performance
Abstract. We study approximating multivariate functions from a reproducing ker-nel Hilbert space wit...
summary:Some characterizations of random approximations are obtained in a locally convex space throu...
A two-player game is sparse if most of its payoff entries are zeros. We show that the problem of com...
International audienceSolving zero-sum matrix games is polynomial, because it boils down to linear p...
We study multiplayer games in which the participants have access to only limited randomness. This co...
We consider the problem of cardinality penalized optimization of a convex func-tion over the probabi...
Let A be a matrix of size N × M (a dictionary) and let ‖ · ‖ be a norm on N. For any data d ∈ N, w...
We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our...
Abstract. Some characterizations of random approximations are obtained in a locally convex space thr...
textSparse approximation problems request a good approximation of an input signal as a linear combi...
We introduce a new technique called oblivious rounding-a variant of randomized rounding that avoids ...
Recent work by Rauhut and Ward developed a notion of weighted sparsity and a corresponding notion of...
performance of the sparsest approximation in a dictionary François Malgouyres⋆ and Mila Nikolova• A...
We examine the problem of approximating the mean of a set of vectors as a sparse linear combination ...
We show that the problem of finding an approximate Nash equilibrium with a polynomial precision is P...
Abstract. We study approximating multivariate functions from a reproducing ker-nel Hilbert space wit...
summary:Some characterizations of random approximations are obtained in a locally convex space throu...
A two-player game is sparse if most of its payoff entries are zeros. We show that the problem of com...
International audienceSolving zero-sum matrix games is polynomial, because it boils down to linear p...
We study multiplayer games in which the participants have access to only limited randomness. This co...
We consider the problem of cardinality penalized optimization of a convex func-tion over the probabi...
Let A be a matrix of size N × M (a dictionary) and let ‖ · ‖ be a norm on N. For any data d ∈ N, w...
We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our...
Abstract. Some characterizations of random approximations are obtained in a locally convex space thr...
textSparse approximation problems request a good approximation of an input signal as a linear combi...
We introduce a new technique called oblivious rounding-a variant of randomized rounding that avoids ...
Recent work by Rauhut and Ward developed a notion of weighted sparsity and a corresponding notion of...
performance of the sparsest approximation in a dictionary François Malgouyres⋆ and Mila Nikolova• A...
We examine the problem of approximating the mean of a set of vectors as a sparse linear combination ...
We show that the problem of finding an approximate Nash equilibrium with a polynomial precision is P...
Abstract. We study approximating multivariate functions from a reproducing ker-nel Hilbert space wit...
summary:Some characterizations of random approximations are obtained in a locally convex space throu...
A two-player game is sparse if most of its payoff entries are zeros. We show that the problem of com...
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