Spring 2027

Introduction to Optimization

Introduction to Optimization covers mathematical optimization from a modeling and solution perspective, focusing on formulating discrete and continuous optimization problems and equilibrium models.

offering recorded3 credits
Recorded instructors · Spring 2027 Jeremy CoulsonJiawei Zhang

Summary

1 / 6

Past student reviews · Michael Ferris, Laurent Lessard

Homework assignments were difficult and time-consuming, with one student noting they would ruin their social life, while another found them helpful for learning.

What you’ll be able to do

  • Engage in topics about "optimization in practice".

  • Use and analyze the results of state of the art optimization software.

  • Use the GAMS modeling system and Jupyter notebooks (in conjunction with elementary Python) or Julia and JUMP.

  • Design good models for realistic applications in engineering and the sciences.

  • Develop a "commercial strength" application of optimization technology.

Source: UW Guide 1 UW Guide 2 UW Guide 3

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(COMP SCI 200, 220, 300, or placement into COMP SCI 300) and (MATH 320, 340, 341, 345, or 375) or graduate/professional standing

COMPSCI/ECE/ISYE 524 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

Recent recorded grades — Spring 2025: 3.45 GPA, 65.6% A/AB (n=90 letter grades).

Historical instructors & teaching patterns

AMANDA SMITH is recorded teaching in Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

JEFFREY LINDEROTH is recorded teaching in Spring 2023, Spring 2024, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

JEREMY COULSON is recorded teaching in Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

LAURENT LESSARD is recorded teaching in Spring 2016, Spring 2017, Spring 2018. Recorded history may be incomplete and does not establish a future schedule.

MICHAEL FERRIS is recorded teaching in Fall 2016, Fall 2019, Fall 2020, Fall 2021, Fall 2022, Fall 2023, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

STEPHEN WRIGHT is recorded teaching in Fall 2018, Spring 2023. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 1000
LEC 002Classroom Instruction0 / 700
LEC 001Classroom Instruction0 / 1000
LEC 002Classroom Instruction0 / 700
LEC 001Classroom Instruction0 / 1000
LEC 002Classroom Instruction0 / 700

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
Download part 1

Student experience

the class

Past student reviews · Stephen Wright, Jeff Linderoth

Students described the course as an insightful introduction to optimization, with lectures that were engaging and made complex topics enjoyable.

Recent recorded grades — Spring 2025: 3.42 GPA, 63.0% A/AB (n=154 letter grades); Fall 2025: 2.99 GPA, 54.4% A/AB (n=79 letter grades); Spring 2026: 3.22 GPA, 56.4% A/AB (n=101 letter grades).

difficulty & workload

Past student reviews · Michael Ferris, Laurent Lessard

Homework assignments were difficult and time-consuming, with one student noting they would ruin their social life, while another found them helpful for learning.

Past student reviews · Jeff Linderoth, Michael Ferris

Some students felt frustrated by strict grade cutoffs and a lack of clear justification for final project scores, despite TAs being encouraging.

Topics

  • Discrete and continuous optimization problems
  • Basic algorithms, data and software tools, modeling languages, and subroutine libraries

Skills

  • Formulating applications as discrete and continuous optimization problems and equilibrium models
  • Using GAMS, Jupyter notebooks with Python, or Julia and JUMP for optimization
  • Analyzing results from state-of-the-art optimization software
  • Designing models for realistic engineering and science applications

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

8 earlier forecasts · 0.17 GPA average error.

Grades over time

Through Spring 2027

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
COMPSCI/ECE/ISYE 524 · Spring 2027

Introduction to Optimization

Recorded 2026-10-14
COMPSCI/ECE/ISYE 524 · Spring 2027

Introduction to Optimization

Recorded 2026-10-14
COMPSCI/ECE/ISYE 524 · Spring 2027

Introduction to Optimization

Recorded 2026-10-14
Raw records 
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Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
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  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
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  "output_id": "92fb88b07269bb02059025056c70d893e7be27d2102a80adfbb56ee4d364411f",
  "requirements_status": "needs_review",
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