Quasi-newton optimization

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Related lectures (29)

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Constraint Satisfaction: Formulation and Algorithms

Covers the formulation of constraint satisfaction problems and systematic algorithms for solving them efficiently.

Introduction to Free Convection: Governing Equations

Explores free convection, laminar flow boundary layer equations, and heat transfer principles.

Proximal Gradient Descent: Optimization Techniques in Machine Learning

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

SGD and Mean Field Analysis

Explores Stochastic Gradient Descent and Mean Field Analysis in two-layer neural networks, emphasizing their iterative processes and mathematical foundations.

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

Faster Gradient Descent: Projected Optimization Techniques

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Simplex Algorithm

Covers the Simplex algorithm for function minimization with linear constraints.

Optimization algorithms

Covers optimization algorithms, focusing on Proximal Gradient Descent and its variations.

Descent methods and line search: Finiteness of the line search algorithm

Explores the Wolfe conditions for line search algorithms and proves the finiteness of the line search parameter.