Fall 2026

Stochastic Programming

STOCHASTIC PROGRAMMING covers decision making under uncertainty, including modeling, algorithms, and applications.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

1 / 5

Historical reviews of Jim Luedtke: Assignments are reported to help students understand class material, though the course is rated as difficult.

Grade history

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

Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
COMPSCI/ISYE 719

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

Prerequisite text tree
  • Graduate/professional standing

Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Historical instructor Jim Luedtke received high praise for lectures that balanced applications and math. Reviewers noted his well-structured lectures and assignments that aided understanding, leading to a strong recommendation for his class.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Fall 2026

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Meeting source records

No records available.

Student experience

the class

Historical reviews of Jim Luedtke: Jim Luedtke is highly recommended, with 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

Historical reviews of Jim Luedtke: Assignments are reported to help students understand class material, though the course is rated as difficult.

Historical reviews of Jim Luedtke: The class is highly recommended, with assignments supporting the understanding of covered topics.

Topics

  • Modeling uncertainty in optimization problems
  • Risk measures and stochastic programming algorithms
  • Approximation and sampling methods

Skills

  • Modeling uncertainty in optimization problems
  • Applying risk measures and stochastic programming algorithms

Grades

Latest available · Spring 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
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Grades over time

Through Spring 2025

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

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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.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "f5d4aa17704717890c6c33137dfe4adefc7106590aab4628330b3bb222ed4f5d",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}