Multi-Objective Optimization: Methods & Solutions

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Page 3 of 4

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.