Newton's method in optimization

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Related lectures (28)

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Gradient Descent

Covers the concept of gradient descent in scalar cases, focusing on finding the minimum of a function by iteratively moving in the direction of the negative gradient.

Newton's Method: Fixed Point Iterative Approach

Covers Newton's method for finding zeros of functions through fixed point iteration and discusses convergence properties.

Descent methods and line search: Quadratic interpolation

Covers Newton's method and quadratic interpolation to find function minima.

Gradient Descent with Momentum

Explores the use of momentum in gradient descent to enhance speed and stability.

Newton's local method: Geometric interpretation

Explores the geometric interpretation of Newton's method in optimization problems.

Newton's Method: Convergence Analysis

Explores the convergence analysis of Newton's method for solving nonlinear equations, discussing linear and quadratic convergence properties.

Iterative Methods for Nonlinear Equations

Explores iterative methods for solving nonlinear equations, discussing convergence properties and implementation details.

Nonlinear Equations: Methods and Applications

Covers methods for solving nonlinear equations, including bisection and Newton-Raphson methods, with a focus on convergence and error criteria.