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 No instructors listed

Summary

1 / 6

Past student reviews · Carla Michini

Homeworks were extremely hard at times, though the total volume was limited to eight assignments. The workload is demanding due to the difficulty of the problems rather than the quantity.

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

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

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

No instructors recorded for this selection.

Historical instructors & teaching patterns

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

The course is described as very challenging, but students reported gaining a lot from the clear and logical presentation of material by the instructor.

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

Homeworks were extremely hard at times, though the total volume was limited to eight assignments. The workload is demanding due to the difficulty of the problems rather than the quantity.

Past student reviews · Carla Michini

Carla Michini was noted for being sweet and kind, making the slides clear and logical. Students found the experience valuable despite the potential difficulty in achieving top grades due to department restrictions.

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 2026-10-14
COMPSCI/ISYE/MATH/STAT 525 · Spring 2027

Linear Optimization

Recorded 2026-10-14
COMPSCI/ISYE/MATH/STAT 525 · Spring 2027

Linear Optimization

Recorded 2026-10-14
COMPSCI/ISYE/MATH/STAT 525 · Spring 2027

Linear Optimization

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:266:004272",
    "course_id": "COMPSCI/ISYE/MATH/STAT 525",
    "course_uid": "course_a04e8d794b24e3b81b984774",
    "term_id": "1274",
    "source_course_id": "004272",
    "source_subject_id": "266",
    "title": "Linear Optimization",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring"
  },
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:490:004272",
    "course_id": "COMPSCI/ISYE/MATH/STAT 525",
    "course_uid": "course_a04e8d794b24e3b81b984774",
    "term_id": "1274",
    "source_course_id": "004272",
    "source_subject_id": "490",
    "title": "Linear Optimization",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring"
  },
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:600:004272",
    "course_id": "COMPSCI/ISYE/MATH/STAT 525",
    "course_uid": "course_a04e8d794b24e3b81b984774",
    "term_id": "1274",
    "source_course_id": "004272",
    "source_subject_id": "600",
    "title": "Linear Optimization",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring"
  },
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:932:004272",
    "course_id": "COMPSCI/ISYE/MATH/STAT 525",
    "course_uid": "course_a04e8d794b24e3b81b984774",
    "term_id": "1274",
    "source_course_id": "004272",
    "source_subject_id": "932",
    "title": "Linear Optimization",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall, Spring"
  }
]
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.

Download part 1Download part 2
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "e26be28d2f60079a57274ade4dac278731175a00bf5bdb47ac73e5427bd89aed",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}