מתודה חדשה לאופטימיזציה חסרת פרמטרים עם תיאום מטריצת הסטיית התקן ומימוש ממדי

תקציר מקורי באנגליתarXiv:2609.38561v1 Announce Type: cross Abstract: Zeroth-order optimization methods are essential for solving black-box problems where gradient information is unavailable or expensive to compute. This paper presents POEM-CMA, a novel parameter-free stochastic zeroth-order algorithm that extends the recent POEM method by integrating covariance matrix alignment and the notion of effective dimension. In contrast to traditional zeroth-order approaches that rely on isotropic random directions, POEM-CMA performs anisotropic sampling by constructing a covariance matrix from gradient estimates. This enables the algorithm to focus sampling efforts on the most informative directions. We introduce the use of the empirical effective dimension $d^* = \frac{\operatorname{tr}(\hat{\Sigma})}{\lambda_{\max