Multi-Objective Optimization: Methods & Solutions

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Related lectures (31)

Explores optimization, simulation, data analysis, and the importance of considering more than just the mean in engineering systems.

Optimization with Constraints: KKT Conditions

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.

Introduction to Optimization

Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.

Recommender Systems: Matrix Factorization

Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.

Simplex Algorithm: Solution on a Vertex

Explores the simplex algorithm and how optimal solutions can be found on vertices of constraint polyhedra.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Optimization and Simulation

Explores optimization, simulation, heuristics, and Pareto solutions in multi-objective problems.

Optimization Principles

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

Optimization without Constraints: Gradient Method

Covers optimization without constraints using the gradient method to find the function's minimum.

Optimization in Large Search Spaces: GPU-Accelerated Join Order

Explores GPU-accelerated join order optimization in large search spaces, leveraging graph topology to reduce computational overheads.

Optimization and Simulation

Covers optimization and simulation techniques for drawing from multivariate distributions and dealing with correlations.

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