Lecture

Computational Aspects of Optimization

Related lectures (29)

Explores heuristic optimization methods to find the global optimum efficiently.

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Bio-Inspired Learning: Neural Networks, Genetic Algorithms

Explores bio-inspired learning with neural networks and genetic algorithms, covering structure, training, and practical applications.

Distributed Intelligent Systems: General Logistics and Course Overview

Covers the general logistics, course rationale, prerequisites, organization, credits, workload, grading, and course content, including swarm intelligence, foraging strategies, and collective phenomena.

Tools for Constraining Neuron Models

Covers tools for constraining neuron models using eFEL and BluePyOpt.

Optimization Methods: Theory Discussion

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

Deep Generative Models in Drug Discovery

Explores the application of deep generative models in drug discovery, focusing on designing small molecules and optimizing molecular structures.

Multi-Criteria Decisions: Pareto Optimization Techniques

Delves into Pareto optimization techniques for generating system configurations and making decisions efficiently.

Optimisation in Energy Systems

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

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Neural Networks: Training and Optimization

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Optimization Techniques: Local Search, VNS, Simulated Annealing

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

Optimization Methods

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

Feed-forward Networks

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

High Performance OPF Solvers

Explores high-performance OPF solvers, addressing challenges in power system optimization and showcasing significant speed-ups and memory-efficient approaches.

Energy Systems Optimization

Explores energy systems modeling, optimization, and cost analysis for efficient operations.

Optimization Principles

Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.

Stochastic Gradient Descent

Explores stochastic gradient descent optimization and the Mean-Field Method in neural networks.

Applications of GAMP

Delves into applying the GAMP algorithm to simplify the lasso problem and analyze optimization challenges in neural networks.