Lectures related to AMPL: Optimization Modeling with AMPLIDE

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

Optimization with Constraints: KKT Conditions

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

Energy optimization strategies

Covers brainstorming options for smart operation changes, heat recovery, and PV panel performance.

Energy Systems Optimization

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

Debugging and Optimization

Covers types of errors in programming and emphasizes the importance of debugging and optimization techniques.

Simulation & Optimization: Poisson Process & Random Numbers

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

Boolean Algebra: Properties and Optimization

Explores Boolean algebra properties and optimization techniques using Karnaugh diagrams and De Morgan's theorems.

Boolean Algebra: Properties and Optimization

Covers Boolean algebra properties, optimization techniques, and the importance of valid groups in Karnaugh maps.

Optimization with Constraints: KKT Conditions

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

MILP Model and Typical Days by FM

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

Introduction to Optimization and Operations Research

Covers fundamental concepts of optimization and operations research, exploring real-world examples and key topics over a semester.

Optimization Programs: Piecewise Linear Cost Functions

Covers the formulation of optimization programs for minimizing piecewise linear cost functions.

Optimisation in Energy Systems

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

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.

Optimisation Strategies: Energy Systems Modelling and Optimization

Explores solving strategies for energy system optimization problems and different types of optimization approaches.

Optimization Techniques: Convexity in Machine Learning

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

Linear Programming: Solving LPs

Covers the process of solving Linear Programs (LPs) using the simplex method.

Optimization Principles

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

Energy System Modeling: Optimization and Performance Indicators

Explores energy system modeling using optimization techniques and performance indicators.

Degree of Freedom Analysis

Explores degree of freedom analysis, redundancy, and data reconciliation in process modeling and optimization.