Stochastic Moving Anchor Algorithms and a Popov's Scheme with Moving Anchor
James Alcala, Yat Tin Chow, Mahesh Sunkula

TL;DR
This paper extends anchoring algorithms for saddlepoint problems to stochastic settings, providing theoretical convergence guarantees and numerical validation, and introduces a novel moving anchor Popov scheme with promising initial results.
Contribution
It introduces stochastic moving anchor algorithms with robust convergence analysis and proposes a new moving anchor Popov scheme with preliminary promising results.
Findings
Stochastic moving anchor algorithms achieve order-optimal convergence rates.
Numerical experiments validate the theoretical convergence guarantees.
A new moving anchor Popov scheme shows promising initial performance.
Abstract
Since their introduction, anchoring methods in extragradient-type saddlepoint problems have inspired a flurry of research due to their ability to provide order-optimal rates of accelerated convergence in very general problem settings. Such guarantees are especially important as researchers consider problems in artificial intelligence (AI) and machine learning (ML), where large problem sizes demand immense computational power. Much of the more recent works explore theoretical aspects of this new acceleration framework, connecting it to existing methods and order-optimal convergence rates from the literature. However, in practice introducing stochastic oracles allows for more computational efficiency given the size of many modern optimization problems. To this end, this work provides the moving anchor variants [1] of the original anchoring algorithms [36] with stochastic implementations…
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Taxonomy
TopicsStochastic Gradient Optimization Techniques · Sparse and Compressive Sensing Techniques · Advanced Optimization Algorithms Research
