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Related lectures (30)

MILP Model and Typical Days by FM

Discusses MILP model, typical days, clustering, and extreme periods analysis in energy systems optimization.

Optimisation in Energy Systems

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

Simulation & Optimization: Poisson Process & Random Numbers

Explores simulation pitfalls, random numbers, discrete & continuous distributions, and Monte-Carlo integration.

Interpreters and Virtual Machines

Explores interpreters and virtual machines, discussing their advantages, types, and optimization techniques.

Linear Optimization Techniques: Problem Solving Approaches

Provides an overview of linear optimization techniques, focusing on problem-solving methods and the importance of constraints and objective functions.

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

Optimization: Mathematical Principles and Algorithms

Covers mathematical principles and algorithms of optimization, using real-world examples and Python implementation.

Computer Architecture: Algorithms to Programs (Compilation)

Explores the transition from algorithms to programs through compilation, emphasizing constraints and machine-understandable coding practices.

Linear Optimization: Directional Derivatives

Explores directional derivatives in linear optimization and their impact on objective functions.

Simplex Algorithm: Solution on a Vertex

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

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