# Linear programming and minimum cost network flows vs nonlinear and discrete optimization

At my college I have an option: To take either of these two classes. My intended career pathway is into quantitative finance and I wanted to know which one would have more use as a quant.

Here is the course description for the nonlinear and discrete optimization class:

Local and global optimality
Optimality conditions
Numerical algorithms
Convex optimization
Duality
Convex relaxation
Integer programming
Branch and bound method
Cutting plane method


Here is the description for the linear programming class:

Introduction Ch. 1 Jan 17
2-4 Formulation and applications of linear programming Ch. 3.1, 3.3-3.12 Jan 22, 24, 29
5-6 Graphical solutions and sensitivity in two-dimensions Ch. 3.2, 5.1, 6.1 Jan 31, Feb 5
7 Interpreting the output of optimization software Ch. 5.2 Feb 7
8-10 The simplex algorithm Ch. 4 Feb 12, 14, 21
11-13 Sensitivity analysis Ch. 5, 6.1-6.4 Feb 26, 28, Mar 5
14-15 Duality Ch 6.5 - 6.10 Mar 7, 12
16 Midterm Mar 14
17-18 Minimum cost network flow: modeling and applications Ch. 8.5 Mar 19, 21
19-21 Special cases of min cost network flow Ch. 7.1, 7.5-7.6, 8.1-8.3 Apr 2, 4, 9
22-24 Shortest path and dynamic programming Ch. 8.2 Apr 11, 16, 18
25-27 Project management


The book for the LP class is : Introduction to Mathematical Programming, 4th Edition, Winston and Venkataramanan.

• In my college the second course you mention was actually a prerequisite for the first one (and your book for the 2d also covers the topics of the first course, although they skip those chapters). Basically they are closely inter related. So in my opinion if you are allowed to take either one, either will be fine and is relevant to quant finance. (Although the most important thing for quant finance is actually Probability and Statistics IMHO). – noob2 Apr 25 '20 at 0:08