Fall 2026

Introduction to Combinatorial Optimization

Introduction to combinatorial optimization covering exact and approximation algorithms for discrete structures.

offering recorded3 credits
Recorded instructors · Fall 2026 Carla Michini3.9/5

Summary

1 / 6

Reviewers describe the workload as heavy, with time-consuming homework and brutal exams. The course requires deep knowledge of in-depth proofs, making it challenging for non-elite math majors.

Grade history

↗
average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

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

COMPSCI/ISYE/MATH 425

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Fall 2026
/5Adjusted rating
/5RMP 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: 4.2/5 raw quality · 2.6/5 difficulty · 5 reviews

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

Carla Michini is praised for her clear explanations of difficult theoretical concepts and genuine care for students. Reviewers describe her as a solid lecturer who inspires enjoyment of the course material.

Some students find her lectures difficult to watch and consider the course extremely challenging. One reviewer notes that the in-depth proofs and high grading standards make the class hard for non-elite math majors.

Recent recorded grades — Fall 2023: 3.01 GPA, 45.6% A/AB (n=68 letter grades); Fall 2024: 3.00 GPA, 40.6% A/AB (n=64 letter grades); Fall 2025: 2.90 GPA, 36.5% A/AB (n=63 letter grades).

Historical instructors & teaching patterns

CARLA MICHINI is recorded teaching in Fall 2018, Fall 2020, Fall 2021, Fall 2023, Fall 2024, Fall 2025. 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 Instruction33 / 1000
LEC 001Classroom Instruction18 / 1000
LEC 001Classroom Instruction39 / 1000

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

Carla Michini is praised for clear explanations and student support, though one reviewer found her lectures hard to watch. Experiences range from easy to extremely difficult depending on the student's background.

Recent recorded grades — Fall 2023: 3.01 GPA, 45.6% A/AB (n=68 letter grades); Fall 2024: 3.00 GPA, 40.6% A/AB (n=64 letter grades); Fall 2025: 2.90 GPA, 36.5% A/AB (n=63 letter grades).

difficulty & workload

Reviewers describe the workload as heavy, with time-consuming homework and brutal exams. The course requires deep knowledge of in-depth proofs, making it challenging for non-elite math majors.

Studying proofs continuously and getting a homework partner can make exams fair and manageable. However, the high grading thresholds and brutal exams are frustrating for some students.

Topics

  • Shortest paths, spanning trees, flows, matchings, and the traveling salesman problem.
  • Structural properties of optimization problems.

Skills

  • Solving discrete optimization problems like shortest paths, flows, and matchings.
  • Analyzing structural properties and applying exact and approximation algorithms.

Grades

Latest available · Fall 2025— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Fall 2025 · 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

1320 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/ISYE/MATH 425 · Fall 2026

Introduction to Combinatorial Optimization

Recorded 2026-09-07
COMPSCI/ISYE/MATH 425 · Fall 2026

Introduction to Combinatorial Optimization

Recorded 2026-09-07
COMPSCI/ISYE/MATH 425 · Fall 2026

Introduction to Combinatorial Optimization

Recorded 2026-09-07
Raw records
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    "term_id": "1272",
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    "source_subject_id": "266",
    "title": "Introduction to Combinatorial Optimization",
    "credits_min": 3,
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    "typically_offered": "Spring"
  },
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    "source_subject_id": "490",
    "title": "Introduction to Combinatorial Optimization",
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  },
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    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:600:004253",
    "course_id": "COMPSCI/ISYE/MATH 425",
    "course_uid": "course_5aa742b184448a57a07564c5",
    "term_id": "1272",
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Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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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.

Download part 1
Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "6b2a92c21a6e98a75630dd8b99d2cb1a5f028a5400470094486c4f186cf8712f",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}