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Gradient Descent in Convex Optimization by alypaty is a document available to read on EtoBox.

What is Gradient Descent in Convex Optimization about?

The document discusses gradient descent as a method for unconstrained optimization, focusing on its implementation, step-size selection, and convergence behavior. It outlines various strategies for choosing step-sizes, including fixed step-size, exact line-search, and backtracking line-search, and provides examples illustrating the behavior of gradient descent in both smooth and non-smooth functions. The lecture emphasizes the importance of understanding the properties of the objective function to effective

Author

alypaty

Language

EN