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MSE 435Practical and Large-Scale Optimization

Units
0.50
Faculty
Engineering
Subject
Management Science and Engineering

When it runs

  1. F24
  2. W25
  3. S25
  4. F25
  5. W26
  6. S26
  7. F26
  8. W27
  9. S27

Ran in 2 of the last 7 terms. Winter 2027: scheduled, 2 lecture sections. Spring 2027: schedule not out yet.

The course focuses on practical optimization that can solve real-world, complex problems. It covers techniques applicable in domains such as data-driven optimization and machine learning. Topics include modelling for resolvability, decomposition and large-scale optimization, nonlinear search methods, soft computing, and metaheuristics. The course emphasizes the design of algorithms and their computer implementation.

Prerequisites

  • Complete 1 of the following
    • Must have completed at least 1 of the following:
      • BME411 - Optimization and Numerical Methods (0.50)
      • CHE521 - Process Optimization (0.50)
      • CO250 - Introduction to Optimization (0.50)
      • ENVE335 - Decision Making for Environmental Engineers (0.50)
      • MSE331 - Introduction to Optimization (0.50)
      • MSE332 - Deterministic Optimization Models and Methods (0.50)
      • SYDE411 - Optimization and Numerical Methods (0.50)
    • Must have completed the following:

Antirequisites

None listed.

Leads to

No course lists it as a requisite.

Winter 2027 sections

Enrolment from UW Open Data, which can trail Quest by up to two days. Instructors and rooms: Schedule of Classes.
SectionClassEnrolledMeets
LEC 00150550 / 84W 14:30–17:20
LEC 701204319 / 9999Online or by arrangement
TUT 10150560 / 84T 13:30–14:20

Reviewed on UW Flow

100% liked it0% easy100% useful

From 1 rating and 1 comment on UW Flow. The most helpful comments, quoted as written, without names. Read them all on UW Flow.

  • This is not an easy course but it is doable. What I kind of liked in this course was that it deals with optimization problems so big that you don't solve the real world problems by hand, you write them out and get a computer to solve them for you. However, there are certain ways which improve how a computer will arrive at an answer and this course is designed to teach a) how to solve the problems and b) the best strategies for solving these problems. This hasn't always been considered in previous courses and I liked that. A little meta trying to optimize an optimization program. I'd recommend this course if anyone wan't to further their optimization skills or is looking for a challenge.

    May 2015 · liked it · 1 found this helpfulOn UW FlowRemove this

Check it elsewhere for Fall 2026

Calendar versions

Undergraduate Calendar