Operations Research (2): Optimization Algorithms

National Taiwan University

Embark on a journey through the realm of Operations Research (OR) with a focus on deterministic optimization techniques. Delve into the world of mathematical and engineering methods applied to various domains such as Business and Management, Economics, Computer Science, Civil Engineering, and Electrical Engineering.

This course, the second part of a captivating series, is designed to equip you with the skills to utilize algorithms for solving different types of optimization programs. Learn to harness the power of the Gurobi solver with Python, gaining the ability to efficiently solve linear programs, integer programs, and nonlinear programs.

• Explore deterministic optimization techniques
• Understand the implementation of algorithms for solving different types of optimization programs
• Learn to use Gurobi solver with Python to efficiently solve linear programs, integer programs, and nonlinear programs

Certificate Available ✔

Course Modules

The Operations Research (OR) Optimization Algorithms course comprises a comprehensive study of efficient algorithms for solving linear programs, integer programs, and nonlinear programs using Gurobi solver with Python.

Course Overview

The first module provides an overview of the course, offering insights into the linear system and Gaussian elimination. Dive into concepts such as linear dependence and independence, and gain a comprehensive understanding of the course structure.

The Simplex Method

Delve into the intricacies of the simplex method in the second module. Explore standard form LPs, basic solutions, and the implementation of the simplex method. Gain practical knowledge through examples and understand the application of Gurobi and Python for LPs.

The Branch-and-Bound Algorithm

Discover the branch-and-bound algorithm in the third module. Learn about linear relaxation, branch and bound, and heuristic algorithms. Gain insights into performance evaluation and explore the application of Gurobi and Python for IPs.

The fourth module introduces gradient descent and Newton's method. Explore the gradient descent algorithm, Newton's method for NLPs, and the application of Gurobi and Python for NLPs. Gain valuable knowledge about these optimization techniques.

Design and Evaluation of Heuristic Algorithms

Explore the design and evaluation of heuristic algorithms in the fifth module. Uncover the three levels of modeling, conceptual and mathematical modeling, and performance evaluation. Gain understanding of heuristic algorithm design and its evaluation.

Course Summary and Future Learning Directions

The final module summarizes the course and provides insights into future learning directions. Gain a comprehensive understanding of the course and get a preview of what lies ahead in the next course. Prepare to embark on a journey of continuous learning.

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