Optimization Methods

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Related lectures (31)

Page 2 of 4

Covers the Finite Element Method, discussing the derivation of the equation of motion and exploring mass and stiffness matrices.

Turbulence: Numerical Flow Simulation

Explores turbulence characteristics, simulation methods, and modeling challenges, providing guidelines for choosing and validating turbulence models.

Newton Method: Convergence and Quadratic Care

Covers the Newton method and its convergence properties near the optimal point.

Strong Convexity and Convergence Rates

Explores strong convexity's role in faster convergence rates for optimization algorithms.

Optimization with Constraints: KKT Conditions

Covers optimization with constraints using KKT conditions and matrix invertibility in numerical analysis.

Gradient Descent: Principles and Applications

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

Gradient Descent: Lipschitz Continuity

Explores Lipschitz continuity in gradient descent optimization and its implications on function optimization.

Explicit Stabilised Methods: Applications to Bayesian Inverse Problems

Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.

Numerical methods: runge-kutta

Covers the Runge-Kutta method and its variations, discussing error minimization and stability in non-linear systems.

Proximal and Subgradient Descent: Optimization Techniques

Discusses proximal and subgradient descent methods for optimization in machine learning.