Fall 2026

Nonlinear Optimization I

Covers theory and algorithms for nonlinear optimization, focusing on unconstrained methods like quasi-Newton and trust-region techniques.

offering recorded3 credits
Recorded instructors · Fall 2026 Stephen Wright3.8/5

Summary

1 / 6

Assignments are graded harshly, and the course is notoriously difficult, requiring significant preparation to keep up with the rigorous material.

Grade history

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average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
COMPSCI/ISYE/MATH/STAT 726 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
  • Graduate/professional standing

Professors

Fall 2026

Stephen Wright

/5Adjusted rating
/5RMP difficulty
captured reviews
About this rating

Raw average: 4.20/5 from 10 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: 3.0/5 raw quality · 4.5/5 difficulty · 2 reviews

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

Stephen Wright is respected and uses his textbooks for lectures, aiding review. However, reviewers note harsh assignment grading and limited office hour responsiveness.

Wright’s lectures focus on rigor over intuition, often transcribing the textbook. Reviewers criticize poor logistics, missing grading criteria, and the course's notorious difficulty.

Recent recorded grades — Fall 2016: 3.38 GPA, 66.0% A/AB (n=47 letter grades); Spring 2018: 3.16 GPA, 50.0% A/AB (n=38 letter grades); Fall 2019: 3.39 GPA, 69.0% A/AB (n=42 letter grades).

Historical instructors & teaching patterns

Historical reviews of Michael Ferris: Stephen Wright is the current instructor, but available reviews describe Michael Ferris. Ferris provided good office hours and reasonable exam weight, yet his lectures were criticized as dull and poorly organized. Students found slides confusing and homework extremely time-consuming, often exceeding 14 hours per assignment.

MICHAEL FERRIS is recorded teaching in Fall 2009, Fall 2011, Fall 2014, Fall 2015, Fall 2018, Spring 2022. Recorded history may be incomplete and does not establish a future schedule.

STEPHEN WRIGHT is recorded teaching in Fall 2006, Fall 2007, Fall 2008, Fall 2013, Fall 2016, Spring 2018, Fall 2019. 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 Instruction13 / 3012
LEC 001Classroom Instruction1 / 302
LEC 001Classroom Instruction11 / 307
LEC 001Classroom Instruction3 / 301

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

Stephen Wright is a respected author whose textbook-based lectures aid review, but students report harsh grading, poor logistics, and a focus on rigor over intuition.

Recent recorded grades — Spring 2025: 3.31 GPA, 52.4% A/AB (n=21 letter grades); Fall 2025: 3.53 GPA, 78.1% A/AB (n=32 letter grades); Spring 2026: 3.35 GPA, 70.0% A/AB (n=20 letter grades).

difficulty & workload

Assignments are graded harshly, and the course is notoriously difficult, requiring significant preparation to keep up with the rigorous material.

Students find the lack of TA contact information and grading criteria frustrating, and note that the professor is not eager to answer questions during office hours.

Topics

  • Unconstrained optimization
  • Bound-constrained optimization
  • Nonlinearly constrained optimization
  • Least-squares problems
  • Nonlinear equations

Skills

  • Nonlinear optimization theory and algorithms
  • Line-search and trust-region methods
  • Quasi-Newton methods
  • Conjugate-gradient and limited-memory methods
  • Derivative-free optimization
  • Least-squares and nonlinear equation algorithms
  • Gradient projection algorithms
  • Penalty methods for constrained optimization

Grades

Latest available · Spring 2026— 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 Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Fall 2026 in UW–Madison.

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/STAT 726 · Fall 2026

Nonlinear Optimization I

Recorded 2026-09-07
COMPSCI/ISYE/MATH/STAT 726 · Fall 2026

Nonlinear Optimization I

Recorded 2026-09-07
COMPSCI/ISYE/MATH/STAT 726 · Fall 2026

Nonlinear Optimization I

Recorded 2026-09-07
COMPSCI/ISYE/MATH/STAT 726 · Fall 2026

Nonlinear Optimization I

Recorded 2026-09-07
Raw records
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Student reviews

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Madgrades

Grade history

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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
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  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "7b0282339fffb829d73ca99df7075cd7db2b845b000c184b71e9bc77620efc8c",
  "requirements_status": "valid",
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