Operations Research (OR) is a field in which people use mathematical and engineering methods to study optimization problems in Business and Management, Economics, Computer Science, Civil Engineering, Electrical Engineering, etc.
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Operations Research (2): Optimization Algorithms
국립 타이완 대학이 강좌에 대하여
For learners who have already taken basic operations research courses. Experience with calculus, linear algebra, and probability is suggested.
배울 내용
Learn how to use algorithms to solve different types of optimization programs.
Learn how to use Gurobi solver with Python to solve these problems efficiently.
귀하가 습득할 기술
- Algorithms
- Business Analytics
- Mathematical Optimization
For learners who have already taken basic operations research courses. Experience with calculus, linear algebra, and probability is suggested.
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국립 타이완 대학
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강의 계획표 - 이 강좌에서 배울 내용
Course Overview
In the first lecture, we briefly introduce the course and give a quick review about some basic knowledge of linear algebra, including Gaussian elimination, Gauss-Jordan elimination, and definition of linear independence.
The Simplex Method
Complicated linear programs were difficult to solve until Dr. George Dantzig developed the simplex method. In this week, we first introduce the standard form and the basic solutions of a linear program. With the above ideas, we focus on the simplex method and study how it efficiently solves a linear program. Finally, we discuss some properties of unbounded and infeasible problems, which can help us identify whether a problem has optimal solution.
The Branch-and-Bound Algorithm
Integer programming is a special case of linear programming, with some of the variables must only take integer values. In this week, we introduce the concept of linear relaxation and the Branch-and-Bound algorithm for solving integer programs.
Gradient Descent and Newton’s Method
In the past two weeks, we discuss the algorithms of solving linear and integer programs, while now we focus on nonlinear programs. In this week, we first review some necessary knowledge such as gradients and Hessians. Second, we introduce gradient descent and Newton’s method to solve nonlinear programs. We also compare these two methods in the end of the lesson.
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