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Mathematics > Optimization and Control

arXiv:2210.05824 (math)
[Submitted on 11 Oct 2022 (v1), last revised 24 Mar 2023 (this version, v2)]

Title:Adapting Zeroth Order Algorithms for Comparison-Based Optimization

Authors:Isha Slavin, Daniel McKenzie
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Abstract:Comparison-Based Optimization (CBO) is an optimization paradigm that assumes only very limited access to the objective function f(x). Despite the growing relevance of CBO to real-world applications, this field has received little attention as compared to the adjacent field of Zeroth-Order Optimization (ZOO). In this work we propose a relatively simple method for converting ZOO algorithms to CBO algorithms, thus greatly enlarging the pool of known algorithms for CBO. Via PyCUTEst, we benchmarked these algorithms against a suite of unconstrained problems. We then used hyperparameter tuning to determine optimal values of the parameters of certain algorithms, and utilized visualization tools such as heat maps and line graphs for purposes of interpretation. All our code is available at this https URL.
Comments: Pending review at SIURO
Subjects: Optimization and Control (math.OC)
Cite as: arXiv:2210.05824 [math.OC]
  (or arXiv:2210.05824v2 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.2210.05824

Submission history

From: Isha Slavin [view email]
[v1] Tue, 11 Oct 2022 23:15:43 UTC (1,804 KB)
[v2] Fri, 24 Mar 2023 01:08:50 UTC (2,341 KB)
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