Operations Research (2): Optimization Algorithms (National Taiwan University)

Course Overview

Operations Research (2): Optimization Algorithms examines how optimization problems get solved, rather than concentrating only on how to formulate them. Offered by National Taiwan University and taught by Ling-Chieh Kung, it covers algorithms for linear, integer, and nonlinear optimization, alongside implementation using Python and Gurobi.

The useful distinction here is between understanding a solver’s underlying methods and simply running software. You study procedures such as simplex and branch-and-bound, then encounter a business application involving service-hub locations and staffing. For data science and analytics learners, this is a route into decision-focused mathematics: choosing an allocation or configuration under constraints, rather than only predicting an outcome. It is also the second course in a broader operations research series.

What You Will Learn

• Linear algebra foundations: Revisit Gaussian elimination, Gauss–Jordan elimination, and linear independence before moving into optimization algorithms.
• Linear programming: Understand standard form and basic solutions, then study the simplex method and how infeasibility or unboundedness affects a problem.
• Integer programming: Use linear relaxation and branch-and-bound to approach problems with integer-valued decision variables.
• Nonlinear optimization: Review gradients and Hessians, examine gradient descent and Newton’s method, and compare the two approaches.
• Solver implementation: Work with Python and Gurobi to solve optimization problems computationally.
• Business application: Examine facility-location decisions involving NEC Taiwan’s service hubs and employee allocation.

Course Structure

The six modules follow a progression from mathematical preparation to algorithms and application:

1. Orientation and algebra review — 7 videos, 1 reading, 1 assignment. The opening material refreshes the matrix operations and independence concepts used later.
2. Linear programming algorithms — 25 videos, 1 assignment. This is the largest module by video count, covering basic solutions, simplex, and problems that lack a finite optimum.
3. Integer optimization — 16 videos, 1 assignment. Attention shifts to relaxation and the branching-and-bounding process.
4. Nonlinear methods — 13 videos, 1 assignment. Gradients and second derivatives lead into two iterative optimization techniques.
5. NEC Taiwan application — 12 videos, 1 assignment. A service-network problem connects hub placement with workforce allocation.
6. Consolidation — 3 videos, 1 assignment. The course closes by revisiting its topics and introducing the direction of further study.

Who Is This Course For?

Consider this course if you study analytics, computer science, engineering, or business and want to understand the algorithms behind optimization software. It is particularly relevant when your questions involve selecting locations, allocating resources, or making decisions subject to mathematical constraints.

The intermediate designation matters. Although the course reviews linear algebra and derivative concepts, that review sits inside an optimization curriculum rather than a full introductory mathematics sequence. Comfort with those ideas should make the algorithm discussions easier to follow.

If your immediate goal is learning Python from scratch, this is not the most direct starting point: Python is used to support optimization work. Likewise, someone seeking a broad machine-learning curriculum should choose a course centered on that subject instead.

Format & Time Commitment

Coursera presents this as a flexible, self-paced course, with an estimate of one week at ten hours of study. The material is organized into six modules, some of which are described in weekly terms; treat those as learning units rather than fixed attendance dates.

Instruction is in English, and there are six assignments. The substantial number of videos means you may prefer to spread the material across a longer period, particularly if you want to work through the mathematics rather than move directly between lectures.

Pros and Cons

Pros

• Algorithm coverage spans three problem families. You can compare linear, integer, and nonlinear approaches within one course.
• Mathematics and software appear together. Python and Gurobi provide a computational counterpart to the algorithm lessons.
• The business case gives the methods context. Facility locations and staffing make the optimization decisions concrete.

Cons

• The mathematical entry point can be demanding. Linear algebra, gradients, and Hessians create a learning hurdle for beginners despite the review material.
• The short workload estimate covers considerable breadth. Learners needing repeated practice may find ten hours an ambitious target.
• The certificate requires the paid experience. Financial aid may help eligible learners, but certificate access should not be confused with unrestricted free participation.

FAQ

Does this course include a certificate?
A shareable certificate is available through the paid Certificate experience. It can be displayed on a professional profile or résumé.

Is this the entire Operations Research Specialization?
No. This listing concerns its second course, focused on algorithms. Coursera states that enrolling also enrolls you in the surrounding Specialization.

Is there a more suitable starting point for learning problem formulation?
The same university offers Operations Research (1): Models and Applications. That is a relevant companion to consider if modeling is your priority.

Can I apply for financial assistance?
The landing page indicates that financial aid is available, subject to eligibility.

Explore the official course page if this mix of optimization mathematics, solver implementation, and business application matches what you want to learn next.