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Introduction
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Topics
3
Motivation
4
Algorithms
5
Convexity
6
Optimality
7
Projections
8
Lower Bounds
9
Explicit Example
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Algebra
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Quadratic
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Gradient Descent
Description:
Dive into a comprehensive 42-minute lecture on optimization techniques presented by Ashia Wilson from MIT as part of the Geometric Methods in Optimization and Sampling Boot Camp. Explore key topics including motivation, algorithms, convexity, optimality, projections, and lower bounds. Gain insights into explicit examples, algebra, quadratic functions, and gradient descent methods. Enhance your understanding of optimization principles and their practical applications in this informative talk from the Simons Institute.

Optimization Crash Course

Simons Institute
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