Fall 2026

Introduction to Optimization

Introduction to mathematical optimization, covering formulation of discrete and continuous problems and equilibrium models, and usage of algorithms and software tools.

offering recorded3 credits
Recorded instructors · Fall 2026 Jeffrey Linderoth

Summary

1 / 6

The course is extremely difficult with long, challenging homework assignments that can take up to 20 hours, requiring substantial independent study time.

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

(COMP SCI 200, 220, 300, 301, 302, 310, or placement into COMP SCI 300) and (MATH 320, 340, 341, 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

Fall 2026

Jeffrey Linderoth delivers enthusiastic, engaging lectures that make complex optimization topics enjoyable and interesting. He provides comprehensive course materials, including recordings and notes, and his TAs are reported as helpful and caring toward students.

Some students criticize Linderoth for an arrogant attitude and for calling out absentees. Others report significant organizational issues, including late or error-prone homework assignments, a lack of guidance on the final project, and unclear evaluation criteria.

Recent recorded grades — Spring 2023: 3.42 GPA, 61.5% A/AB (n=13 letter grades); Spring 2024: 3.21 GPA, 55.0% A/AB (n=151 letter grades); Spring 2025: 3.37 GPA, 59.4% A/AB (n=64 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

Historical reviews of Laurent Lessard: Laurent Lessard is described as a clear, organized, and caring instructor who structures lectures well and answers questions effectively. Reviewers found his homework difficult but helpful for learning, and they highly recommended his teaching.

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.

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

Fall 2026

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction77 / 1300
LEC 001Classroom Instruction22 / 1300
LEC 001Classroom Instruction23 / 1300

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

Meeting source records
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Student experience

the class

Jeffrey Linderoth delivers engaging, enthusiastic lectures that make complex optimization topics accessible, though students report significant variability in his demeanor and course organization.

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

The course is extremely difficult with long, challenging homework assignments that can take up to 20 hours, requiring substantial independent study time.

Students appreciate posted recordings and helpful TAs, but criticize disorganized project guidance, unclear evaluation criteria, and occasional arrogant or sidetracked teaching styles.

Topics

  • Discrete and continuous optimization problems
  • Equilibrium models
  • Optimization algorithms and software tools

Skills

  • Formulating applications as optimization and equilibrium models
  • Using algorithms, data structures, and software tools for optimization

Grades

Historical instructor

Fall 2026 · 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 · 0.10 GPA average error.

Grades over time

Through Fall 2026

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

1283 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.

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Selected offering source records
COMPSCI/ECE/ISYE 524 · Fall 2026

Introduction to Optimization

Recorded 2026-09-07
COMPSCI/ECE/ISYE 524 · Fall 2026

Introduction to Optimization

Recorded 2026-09-07
COMPSCI/ECE/ISYE 524 · Fall 2026

Introduction to Optimization

Recorded 2026-09-07
Raw records
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    "source_subject_id": "266",
    "title": "Introduction to Optimization",
    "credits_min": 3,
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  },
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    "source_subject_id": "320",
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    "credits_min": 3,
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    "typically_offered": "Fall, Spring"
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  {
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    "course_id": "COMPSCI/ECE/ISYE 524",
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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 2026-09-07

Download the original dataset
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "5cf26f77a13151941d3a9b998009fcbc2dbc9a81553ef458291e9dca814080ba",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}