Stochastic Programming
Covers stochastic programming for decision making under uncertainty, including modeling, risk measures, algorithms, and sampling methods.
Summary
Past student reviews · Jim Luedtke
Assignments were reported to be helpful in understanding class material, though the difficulty rating was noted as high.
What you’ll be able to do
Learn the terms, basic capabilities, and limitations of stochastic programming models
Formulate stochastic programming models
Implement the algorithms used to solve stochastic programming problems
Learn principles of decomposition algorithms for solving large-scale optimization problems
Source: UW Guide 1· UW Guide 2
Grade history
↗All recorded terms · compare terms & instructors
Prerequisites
Course mapGraduate/professional standing
- Graduate/professional standing
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Graduate/professional standing
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Past student reviews · Jim Luedtke
Historical feedback describes the course as one of the best at UW, noting well-structured lectures that balance applications and math effectively.
Recent recorded grades — Spring 2020: 3.62 GPA, 69.2% A/AB (n=13 letter grades); Fall 2023: 3.78 GPA, 82.6% A/AB (n=23 letter grades); Spring 2025: 3.68 GPA, 70.6% A/AB (n=17 letter grades).
difficulty & workload
Past student reviews · Jim Luedtke
Assignments were reported to be helpful in understanding class material, though the difficulty rating was noted as high.
Past student reviews · Jim Luedtke
A past student highly recommended the class, specifically praising the instructor's ability to strike a right balance between theory and practical applications.
Topics
- Modeling uncertainty, risk measures, stochastic programming algorithms, approximation and sampling methods, and applications
Skills
- Formulating and understanding stochastic programming models
- Implementing algorithms and decomposition principles for optimization
Grades
Latest available · Spring 2025— not enough history to project Spring 2027.
Grade distribution · % of letter grades
Grades over time
Through Spring 2025
More grade details Grade mix, volume & source data
Not enough comparable courses for Spring 2027 in UW–Madison.
Sources & history
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.
Selected offering source records
No offering records for the selected term.
Raw records
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Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "1251def70dc5e7ca7178008926f5d32dc90e4e437bd760ed979ea51a62f3bc59",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}