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Randomized Derivative Free Optimization via CMA-ES and Sparse Techniques - Applications to Radars

Konstantinos Varelas 1, 2, 3
3 RANDOPT - Randomized Optimisation
CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique, Inria Saclay - Ile de France
Abstract : In this thesis, we investigate aspects of adaptive randomized methods for black-box continuous optimization. The algorithms that we study are based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm and focus on large scale optimization problems. We start with a description of CMA-ES and its relation to the Information Geometric Optimization (IGO) framework, succeeded by a comparative study of large scale variants of CMA-ES. We furthermore propose novel methods which integrate tools of high dimensional estimation within CMA-ES, to obtain more efficient algorithms for large scale partially separable problems. Additionally, we describe the methodology for algorithm performance evaluation adopted by the Comparing Continuous Optimizers (COCO) platform, and finalize the bbob-largescale test suite, a novel benchmarking suite with problems of increased dimensions and with a low computational cost. Finally, we present the formulation, methodology and obtained results for two applications related to Radar problems, the Phase Code optimization problem and the Phased-Array Pattern design problem.
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https://hal.inria.fr/tel-03152343
Contributor : Konstantinos Varelas <>
Submitted on : Thursday, February 25, 2021 - 3:07:18 PM
Last modification on : Friday, February 26, 2021 - 3:30:09 AM

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Konstantinos Varelas. Randomized Derivative Free Optimization via CMA-ES and Sparse Techniques - Applications to Radars. Optimization and Control [math.OC]. IP Paris, 2021. English. ⟨tel-03152343⟩

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