Add to ORKGWe provide conditions that guarantee local rates of convergence in distribution of iterated random functions that are not nonexpansive mappings in locally compact Hadamard spaces. Our results are applied to stochastic instances of common algorithms in optimization, stochastic tomography for X-FEL imaging, and a stochastic algorithm for the computation of Fr\'echet means in model spaces for phylogenetic trees.Comment: 29 pages, 74 references. This is a more narrowly focused version of arXiv:2007.06479. arXiv admin note: text overlap with arXiv:2205.1589
We improve the results from [Lindvall, T., 1979. On coupling of discrete renewal processes. Z. Wahrs...
grantor: University of TorontoQuantitative geometric rates of convergence for reversible M...
We show that if a sequence of piecewise affine linear processes converges in the strong sense with a...
We provide conditions that guarantee local rates of convergence in distribution of iterated random f...
We study the convergence of random function iterations for finding an invariant measure of the corre...
Given a sequence (mn) of random probability measures on a metric space S, consider the conditions: ...
We apply recent results in Markov chain theory to Hastings and Metropolis algorithms with either ind...
Summary. This is an expository paper which presents various ideas related to non-asymptotic rates of...
The vast majority of convergence rates analysis for stochastic gradient methods in the literature fo...
Consider a contraction operator $T$ over a complete metric space $\mathcal X$ with the fixed point $...
Summary. Bounds on convergence rates for Markov chains are a very widely-studied topic, motivated la...
summary:We deal with a stochastic programming problem that can be inconsistent. To overcome the inco...
To sample from distributions in high dimensional spaces or finite large sets di-rectly is not feasib...
Diagnosing convergence of Markov chain Monte Carlo is crucial and remains an essentially unsolved pr...
We analyze the convergence rates of stochastic gradient algorithms for smooth finite-sum minimax opt...
We improve the results from [Lindvall, T., 1979. On coupling of discrete renewal processes. Z. Wahrs...
grantor: University of TorontoQuantitative geometric rates of convergence for reversible M...
We show that if a sequence of piecewise affine linear processes converges in the strong sense with a...
We provide conditions that guarantee local rates of convergence in distribution of iterated random f...
We study the convergence of random function iterations for finding an invariant measure of the corre...
Given a sequence (mn) of random probability measures on a metric space S, consider the conditions: ...
We apply recent results in Markov chain theory to Hastings and Metropolis algorithms with either ind...
Summary. This is an expository paper which presents various ideas related to non-asymptotic rates of...
The vast majority of convergence rates analysis for stochastic gradient methods in the literature fo...
Consider a contraction operator $T$ over a complete metric space $\mathcal X$ with the fixed point $...
Summary. Bounds on convergence rates for Markov chains are a very widely-studied topic, motivated la...
summary:We deal with a stochastic programming problem that can be inconsistent. To overcome the inco...
To sample from distributions in high dimensional spaces or finite large sets di-rectly is not feasib...
Diagnosing convergence of Markov chain Monte Carlo is crucial and remains an essentially unsolved pr...
We analyze the convergence rates of stochastic gradient algorithms for smooth finite-sum minimax opt...
We improve the results from [Lindvall, T., 1979. On coupling of discrete renewal processes. Z. Wahrs...
grantor: University of TorontoQuantitative geometric rates of convergence for reversible M...
We show that if a sequence of piecewise affine linear processes converges in the strong sense with a...