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The future of optimization: How “learn to optimize” is reshaping algorithm design and configuration - EurekAlert
<p>In a review article published in National Science Review, Ke Tang from Southern University of Science and Technology and Xin Yao from Lingnan University present an overview of the “Learn to Optimize” (L2O) paradigm, which leverages machine learning methodologies to achieve efficient and high-quality automatic configuration and design of optimization algorithms. The article discusses the key ideas, approaches, theoretical foundations, successful application cases, and generalization issues of L2O.</p>
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