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Related lectures (32)

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Optimality of Convergence Rates: Accelerated Gradient Descent

Explores the optimality of convergence rates in convex minimization problems using accelerated gradient descent methods.

Spectral Gap in Markov Chains

Explores the spectral gap in Markov chains and its impact on convergence speed.

Optimization Basics: Linear Algebra, Analysis, Convexity

Introduces optimization basics, covering linear algebra, analysis, and convexity principles.

Newton's Method: Optimization & Indefiniteness

Covers Newton's Method for optimization and discusses the caveats of indefiniteness in optimization problems.

Gradient Descent Methods: Theory and Computation

Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.

Curve Fitting: Polynomial Interpolation

Explores curve fitting via polynomial interpolation, stability, errors, and node distribution impact.

Conjugate Gradient Optimization

Explores Conjugate Gradient optimization, covering quadratic and nonlinear cases, Wolfe conditions, BFGS, CG algorithms, and matrix symmetry.

Optimality of Convergence Rates: Accelerated Gradient Descent

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.

Gradient Descent: Principles and Applications

Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.

Developments Limits: Re-discover

Explains the procedure to find limits of functions and series.