Optimization Methods

Learning Outcomes:

• Formulating and solving shortest path problems in graphs
• Implementing network flow algorithms like Ford-Fulkerson and Edmonds-Karp
• Applying linear programming techniques to network flow problems
• Understanding convex sets, functions, and optimization principles
• Utilizing gradient descent methods for unconstrained optimization
• Employing stochastic optimization techniques like SGD
• Solving constrained optimization problems using projected gradient descent and Frank-Wolfe algorithm
• Analyzing duality and applying Lagrange multipliers in optimization
• Interpreting KKT conditions for optimality in constrained problems
• Modeling real-world scenarios as optimization problems across various domains

Skills for module:

Python

Matlab

Algorithms

Data Structures

Linear Algebra

Calculus

Mathematics

Machine Learning

Data Science

Artificial Intelligence

Neural Networks

Deep Learning

Problem Solving

Critical Thinking

Time Management