About this course
The first module of the course is a general overview of key concepts in linear algebra, calculus, and optimization. The second module of the course is on linear optimization, covering modeling techniques with many applications, basic polyhedral theory, simplex method, and duality theory. The third module is on convex conic optimization, which is a significant generalization of linear optimization. The fourth and final module focuses on integer optimization, which augments the previously covered optimization models with the flexibility of integer decision variables.
What you'll learn
- Modeling skills for formulating various analytics problems as linear, convex nonlinear, and integer optimization problems.
- Basic optimization theory including duality theory and convexity theory, which will provide a deeper understanding of not only how to formulate an optimization model, but also when and why.
- Fundamental algorithmic schemes for solving linear, nonlinear, and integer optimization problems.
- Computational skills for implementing and solving an optimization problem using modern optimization modeling language and solvers.
- Module 1: Introduction
- Module 2: Illustration of the Optimization Problems
- Module 3: Review of Mathematical Concepts
- Module 4: Convexity
- Module 5: Outcomes of Optimization
- Module 6: Optimality Certificates
- Module 7: Unconstrained Optimization: Derivate Based
- Module 8: Unconstrained Optimization: Derivative Free
- Module 9: Linear Optimization Modeling – Network Flow Problems
- Module 10: Linear Optimization Modeling – Electricity Markets
- Module 11: Linear Optimization Modeling – Decision-Making Under Uncertainty
- Module 12: Linear Optimization Modeling – Handling Nonlinearity
- Module 13: Geometric Aspects of Linear Optimization
- Module 14: Algebraic Aspect of Linear Optimization
- Module 15: Simplex Method in a Nutshell
- Module 16: Further Development of Simplex Method
- Module 17: Linear Programming Duality
- Module 18: Robust Optimization
- Module 19: Nonlinear Optimization Modeling – Approximation and Fitting
- Module 20: Nonlinear Optimization Modeling – Statistical Estimation
- Module 21: Convex Conic Programming – Introduction
- Module 22: Second-Order Conic Programming – Examples
- Module 23: Second-Order Conic Programming – Advanced Modeling
- Module 24: Semi-definite Programming – Advanced Modeling
- Module 25: Discrete Optimization: Introduction
- Module 26: Discrete Optimization: Modeling with binary variables - 1
- Module 27: Discrete Optimization: Modeling with binary variables – 2
- Module 28: Discrete Optimization: Modeling exercises
- Module 29: Discrete Optimization: Linear programming relaxation
- Module 30: Discrete Optimization: Solution methods
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Who can take this course?
Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.