Spring 2027

Linear Optimization

Linear Optimization covers problems with linear inequality constraints, emphasizing formal proofs, the simplex method's theory, duality, and theorems of the alternatives.

offering recorded3 credits
Recorded instructors · Spring 2027 Carla Michini3.9/5

Summary

1 / 6

Past student reviews · Carla Michini

Past students noted that exams required deep understanding of proofs and homework, as they were not allowed note sheets or textbook access during tests.

What you’ll be able to do

  • Use linear programming to formulate real world decision problems.

  • Apply algorithms to solve linear programming problems and demonstrate their correctness.

  • Combine different proving techniques explored in class in an original way to show new results.

Source: UW Guide 1 UW Guide 2 UW Guide 3 UW Guide 4

Grade history

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average GPA
letter grades
A
AB
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All recorded terms · compare terms & instructors

Prerequisites

Course map

MATH 320, 340, 341, 345, 375, or443 or graduate/professional standing or member of the Pre-Masters Mathematics (Visiting International) Program

COMPSCI/ISYE/MATH/STAT 525 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
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.42/5 from 12 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 4.6/5 raw quality · 3.7/5 difficulty · 7 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Past student reviews · Carla Michini

Carla Michini taught a challenging course where exams constituted 60–70% of the grade and required deep understanding of proofs without textbook access. Students noted that homeworks were extremely hard, though there were only eight of them.

Michini was described as accessible and supportive, inviting questions during class and office hours. While one student found the content boring, others praised her clear slides and strong grasp of optimization.

Recent recorded grades — Spring 2024: 3.16 GPA, 46.7% A/AB (n=60 letter grades); Spring 2025: 3.02 GPA, 43.2% A/AB (n=74 letter grades); Spring 2026: 3.12 GPA, 46.2% A/AB (n=52 letter grades).

Historical instructors & teaching patterns

Past student reviews · Stephen Wright

Past students reported that Stephen Wright conveyed linear programming principles effectively, noting that his midterm was easy while the final was more difficult. Another student described him as forthright and enjoyable to work with. These reviews highlight a focus on semi-tough proofs and helpful office hours.

ALBERTO DEL PIA is recorded teaching in Fall 2017, Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2023, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule.

CARLA MICHINI is recorded teaching in Spring 2018, Spring 2019, Spring 2020, Spring 2022, Fall 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

JESSE THOMAS HOLZER is recorded teaching in Fall 2013. Recorded history may be incomplete and does not establish a future schedule.

MICHAEL FERRIS is recorded teaching in Fall 2006, Fall 2007, Fall 2008, Spring 2010, Spring 2012, Spring 2013, Spring 2016. Recorded history may be incomplete and does not establish a future schedule.

SHI JIN is recorded teaching in Spring 2015. Recorded history may be incomplete and does not establish a future schedule.

STEPHEN WRIGHT is recorded teaching in Spring 2008, Spring 2009, Fall 2009, Fall 2010, Fall 2012, Spring 2014, Fall 2014, Fall 2015, Spring 2017. 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 / 850
LEC 001Classroom Instruction0 / 850
LEC 001Classroom Instruction0 / 850
LEC 001Classroom Instruction0 / 850

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 · Carla Michini

Students found the linear optimization content super interesting, citing Carla Michini's strong grasp of the field and her profound insights during lectures.

Recent recorded grades — Spring 2025: 3.02 GPA, 43.2% A/AB (n=74 letter grades); Fall 2025: 2.90 GPA, 39.1% A/AB (n=64 letter grades); Spring 2026: 3.12 GPA, 46.2% A/AB (n=52 letter grades).

difficulty & workload

Past student reviews · Carla Michini

Past students noted that exams required deep understanding of proofs and homework, as they were not allowed note sheets or textbook access during tests.

Past student reviews · Carla Michini

Although the course was challenging, students appreciated how Carla Michini made the slides clear and logical, helping them gain significant knowledge.

Topics

  • Optimization problems with linear inequality constraints
  • Theory behind the simplex method
  • Duality theory and theorems of the alternatives

Skills

  • Formulate real-world decision problems using linear programming
  • Apply algorithms to solve linear programming problems and demonstrate correctness
  • Combine proving techniques to show new mathematical results

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 · 0.15 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/ISYE/MATH/STAT 525 · Spring 2027

Linear Optimization

Recorded 2026-10-29
COMPSCI/ISYE/MATH/STAT 525 · Spring 2027

Linear Optimization

Recorded 2026-10-29
COMPSCI/ISYE/MATH/STAT 525 · Spring 2027

Linear Optimization

Recorded 2026-10-29
COMPSCI/ISYE/MATH/STAT 525 · Spring 2027

Linear Optimization

Recorded 2026-10-29
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 2026-10-29

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.

Download part 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "2273909878694f1725af14f9588a10efc9fa5c157fd7a950a6ed0f42d21552f3",
  "requirements_status": "needs_review",
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