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What is Elementary Descent Methods in Optimization about?

This document discusses elementary descent methods for optimization. It introduces gradient descent and analyzes its convergence properties for smooth convex functions. Gradient descent takes steps proportional to the negative gradient to minimize an objective function at each iteration. The document proves convergence rates for gradient descent under convexity assumptions on the objective.

Author

Subhajit Kundu

Language

EN