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Concept

Local search (optimization)

Related lectures (29)

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.

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 MOOC: Optimization: principles and algorithms - Network and discrete optimization MOOC: Optimization: principles and algorithms - Unconstrained nonlinear optimization MOOC: Optimization: principles and algorithms - Linear optimization

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

Optimization and Simulation: Simulated Annealing

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

Optimization Methods in Machine Learning

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

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.

Heuristic Optimization Methods

Explores heuristic optimization methods to find the global optimum efficiently.

Stochastic Optimization and Adaptive Gradient Methods

Explores stochastic optimization, adaptive gradient methods, recommender systems, and matrix factorization in user-item rating matrices.

Optimization: Gradient Descent and Subgradients

Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.

Optimisation in Energy Systems

Explores optimization in energy system modeling, covering decision variables, objective functions, and different strategies with their pros and cons.

Optimization Methods

Covers unconstrained and constrained optimization, optimal control, neural networks, and global optimization methods.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Thermodynamic Properties: Equations and Models

Explains thermodynamic properties, equations of state, and mixture rules for energy systems modeling.

Ant Colony Optimization: Routing and Optimization Log in to Mediaspace to watch this video

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

Lipschitz Gradient Theorem Log in to Mediaspace to watch this video

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

Riemannian gradient descent Log in to Mediaspace to watch this video

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

Relaxation-based Retiming Log in to Mediaspace to watch this video

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