Computational Aspects of Optimization

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Related lectures (29)

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Heuristic Optimization Methods

Explores heuristic optimization methods to find the global optimum efficiently.

Optimising Neuron Models: eFEL and BluePyOpt

Introduces eFEL and BluePyOpt for optimizing neuron models with experimental data, using evolutionary algorithms.

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.