Convergence of zeroth-order proximal point algorithms in the high-temperature regime
Emanuele Naldi, Hippolyte Labarri\`ere, Cesare Molinari, Silvia Villa

TL;DR
This paper analyzes the convergence of zeroth-order proximal point algorithms at fixed positive temperatures, providing theoretical guarantees and connecting smoothed and original objectives, especially for high-temperature regimes.
Contribution
It offers a comprehensive convergence analysis of ZOPO and ZOPPA at fixed temperatures, addressing variance issues and linking smoothed and original objectives.
Findings
Proves convergence of ZOPPA under minimal assumptions.
Establishes explicit guarantees connecting smoothed and original objectives.
Provides convergence results for the sampled method at fixed temperature.
Abstract
Efficient methods for non-convex black-box optimization largely rely on sampling. In this context, the Zeroth-Order Proximal Operator (ZOPO) and the corresponding Zeroth-Order Proximal Point Algorithm (ZOPPA) have attracted significant interest, as they combine the advantage of requiring only objective evaluations with the powerful theoretical framework of proximal point algorithms. ZOPO depends on a temperature parameter which, when going to zero, reduces ZOPO to the exact proximal operator. By exploiting this property, the vanishing-temperature regime has been leveraged in several works to obtain theoretical guarantees via inexact proximal methods. However, this regime is computationally unsustainable when sampling is used to estimate ZOPO, since the corresponding Monte Carlo estimators suffer from severe variance issues. We therefore propose a comprehensive analysis of ZOPO for any…
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