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Stochastic optimization: algorithms and convergence

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Stochastic optimization is a cutting-edge discipline, essential for solving complex problems where uncertainty and variability of data render classical methods inadequate. This approach finds applications in diverse fields such as finance, engineering, and machine learning. By combining robust mathematical tools with sophisticated algorithms, stochastic optimization enables the identification of optimal or near-optimal solutions despite the … Read more

Convex optimization: methods and algorithms

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In short: Foundations of convex optimization: principles and role of convex functions Convex optimization occupies a privileged place in the field of mathematical optimization, thanks to its analytical properties and convergence guarantees. It focuses on minimizing convex functions over convex sets, where convexity ensures a functional landscape without locally deceptive “peaks” or “valleys.” In practice, … Read more