Lectures related to Local search (optimization)

Constraint Satisfaction: Formulation and Algorithms

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

Optimization Techniques: Local Search, VNS, Simulated Annealing

Explores optimization techniques like local search, VNS, and simulated annealing.

Ant Colony Optimization: Routing and Optimization

Explores Ant Colony Optimization (ACO) for routing and optimization, discussing constructive heuristics, local search, pheromone mechanisms, and real-world applications.

Lipschitz Gradient Theorem

Covers the Lipschitz gradient theorem and its applications in function optimization.

Optimization and Simulation: Heuristics and Neighborhoods

Explores greedy heuristics, neighborhoods in optimization, and local search algorithms.

Optimization and Simulation: Heuristics Intensification

Explores optimization and simulation techniques, emphasizing heuristics intensification for efficient solution finding.

Optimization Methods

Covers the Newton's local method in Python using NumPy for optimization.

Riemannian gradient descent

Explores Riemannian gradient descent, covering Taylor expansions, optimality conditions, algorithm templates, line search, and critical points.

Relaxation-based Retiming

Covers relaxation-based retiming to optimize cycle-time by shortening paths with excessive delays.

Optimization and Simulation: Simulated Annealing

Explores simulated annealing for optimization, emphasizing parameter tuning and diversification to escape local minima.

Maximal Solutions in Advanced Analysis II

Explores the concept of maximal solutions in advanced analysis.

Lagrange Multipliers: Optimization in 2 Variables

Explores Lagrange multipliers for optimizing functions in 2 variables, emphasizing global and local extrema.

Optimization Methods in Machine Learning

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.

Nonlinear Optimization: Newton's Method

Explores nonlinear optimization, focusing on Newton's method and descent methods for finding optimal solutions efficiently.

Cheeger's Inequalities

Explores Cheeger's inequalities for random walks on graphs and their implications.

Binary Search Algorithm

Explores the binary search algorithm's efficiency in reducing search time.

Nonlinear Systems: Seeking Solutions

Explores the search for solutions in nonlinear systems through various methods and techniques.

Grover Algorithm: Search Optimization

Covers the Grover algorithm, a quantum search algorithm that provides a quadratic speedup.

Convex Sets: MGT-418 Lecture

On Convex Optimization covers course organization, mathematical optimization problems, solution concepts, and optimization methods.

Heuristic Optimization Methods

Explores heuristic optimization methods to find the global optimum efficiently.