Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Concept

Interior-point method

Related lectures (31)

Optimisation with Constraints: Interior Point Algorithm

Explores optimization with constraints using KKT conditions and interior point algorithm on two examples of quadratic programming.

Primal-dual Methods for Composite Minimization

Covers primal-dual methods for composite minimization, stochastic algorithms, nonconvex problems, and quadratic penalty techniques.

Primal-dual optimization: Theory and Computation

Explores primal-dual optimization, conjugation of functions, strong duality, and quadratic penalty methods in data mathematics.

Trajectory Optimization with CALIPSO

Introduces CALIPSO, a solver for trajectory optimization with constraints in robotics, showcasing methods and examples.

Optimality Conditions in Linear Optimization MOOC: Optimization: principles and algorithms - Linear optimization

Covers optimality conditions, strong duality, and complementarity slackness in linear optimization.

Unweighted Bipartite Matching

Introduces unweighted bipartite matching and its solution using linear programming and the simplex method.

Hedging for LPs

Covers the concept of hedging for Linear Programs and the simplex method, focusing on minimizing costs and finding optimal solutions.

Quadratic Penalty Methods: Concepts and Algorithms

Explores quadratic penalty methods for nonconvex-concave optimization problems and introduces primal-dual algorithms with penalty functions.

Primal-dual Optimization II

Explores primal-dual optimization methods, focusing on Lagrangian approaches and various methods like penalty, augmented Lagrangian, and splitting techniques.

Primal-dual Optimization III: Lagrangian Gradient Methods

Explores primal-dual optimization methods, emphasizing Lagrangian gradient techniques and their applications in data optimization.

Primal-dual Optimization: Fundamentals

Explores primal-dual optimization, minimax problems, and gradient descent-ascent methods for optimization algorithms.

Linear Programming Duality

Explores linear programming duality, covering constraints, variables, solutions, and the relationship between primal and dual LP.

Primal-dual Optimization: Methods and Applications

Explores primal-dual optimization methods, algorithms, convergence, and applications in nonconvex optimization and image deconvolution.

Primal-dual Optimization: Lagrangian Methods

Explores primal-dual optimization with a focus on Lagrangian methods and their convergence, drawbacks, and enhancements.

Linear Programming: Weighted Bipartite Matching

Covers linear programming, weighted bipartite matching, and vertex cover problems in optimization.

Primal-dual Optimization: Extra-Gradient Method

Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.

Formulation, Properties: Infimum vs Optimum MOOC: Optimization: principles and algorithms - Unconstrained nonlinear optimization MOOC: Optimization: principles and algorithms - Linear optimization

Explains the difference between infimum and optimum in optimization problems and how to find the optimal solution.

Optimization with Constraints: KKT Conditions

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

Linear Programming: Two-phase Simplex Algorithm Log in to Mediaspace to watch this video

Covers the application of the two-phase Simplex algorithm to solve linear programming problems.

Nonlinear Programming: Part I Log in to Mediaspace to watch this video

Covers the fundamentals of Nonlinear Programming and its applications in Optimal Control, exploring techniques, examples, optimality definitions, and necessary conditions.