MIT OpenCourseWare

Browse materialsMIT 15.093J · Fall 2009

Optimization Methods

Subjects: Management · Mathematics

Art: Gaston, John, (John Gaston merchant ledger) - DPLA -…, 1773 · Public domain

Instructor: Prof. Dimitris Bertsimas
Term: Fall 2009
Level: Graduate
What's inside: 1 lecture note · 1 reading · 52 other files · 4 web pages
Materials open: MIT OCW

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About this course

Source: MIT OpenCourseWare 15.093J Optimization Methods, Fall 2009, Prof. Dimitris Bertsimas — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

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Readings

Freund, Robert. “Introduction to Semidefinite Programming.” (Used with permission.)PDF Download

Lecture Notes

24 files

Recitations

11 files

Assignments

8 files

Exams

Other materials from this course

MIT OpenCourseWare, Prof. Dimitris Bertsimas, 15.093J Optimization Methods, Fall 2009.

License: CC BY-NC-SA 4.0 · Edition: Fall 2009 · Open the original course on OCW ↗

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Optimization Methods

Before you start

What's here, and what isn't

Not included here

Materials by section

Readings

Lecture Notes

Recitations

Assignments

Exams

Other materials from this course

Lecture 1: Applications of linear optimization

Lecture 2: Geometry of linear optimization

Lecture 3: Simplex method I

Lecture 4: Simplex method II

Lecture 5: Duality theory I

Lecture 6: Duality theory II

Lecture 7: Sensitivity analysis

Lecture 8: Robust optimization

Lecture 9: Large scale optimization

Lecture 10: Network flows I Courtesy of Prof. Andreas Schulz. Used with permission.

Lecture 11: Network flows II Courtesy of Prof. Andreas Schulz. Used with permission.

Lecture 12: Applications of discrete optimization

Lecture 13: Branch and bound and cutting planes

Lecture 14: Lagrangean methods

Lecture 15: Heuristics and approximation algorithms

Lecture 16: Dynamic programming

Lecture 17: Applications of nonlinear optimization

Lecture 18: Optimality conditions and gradient methods

Lecture 19: Line searches and Newton’s method

Lecture 20: Conjugate gradient methods

Lecture 21: Affine scaling algorithm

Lecture 22: Interior point methods

Lecture 23: Semidefinite optimization I

Lecture 24: Semidefinite optimization II