ISM 206 - Spring 2005

General Class Information

A first graduate course in optimization with an emphasis on problems arising in management and engineering applications. This class will focus on problem formulation, software technologies and analytical methods for optimization. This serves as an introduction to a wide variety of optimization problems and techniques including linear and nonlinear programming, dynamic programming, network flows, integer programming and decision analysis.

Lecture times:

Tuesday and Thursday, 10-11:45am

Baskin Engineering Room 169

Instructor:

Name: Kevin Ross (kross@soe.ucsc.edu)

Phone: 831-459-1878

Office: E2 Room 367

Instructor Office Hours:

Tuesday 2-4pm or by appointment

Handouts

• Syllabus including prerequisites, a schedule of classes and required readings.
• Homework
• Homework 1 due in class 6pm April 7. Updated Tues 04-05!
• Homework 2 due in class 6pm April 14.
• Homework 3 due in class 6pm April 28
• Homework 4 due in class 10am May 12
• Homework 5 due in class Tues May 31
• Lecture notes Powerpoint
• Lecture 1 Introduction
• Lecture 2 Linear Programming
• Lecture 3 Simplex Method
• Lecture 4 Duality and Sensitivity Analysis
• Lecture 5 Alternative Methods and Network Problems
• Lecture 6 Nonlinear Unconstrained Optimization
• Lecture 7 Nonlinear Objective with Linear Constraints
• Lecture 8 Nonlinear Objective with Nonlinear Constraints
• Lecture notes pdf
1. Some of these are subject to change as I am working with the scribe to correct points.
2. Please send me the original latex files for these, and I will post the pdf
• Lecture 1 Introduction
• Lecture 2 Linear Programming
• Lecture 3 Simplex Method
• Lecture 4 Duality and Sensitivity Analysis
• Lecture 5 Alternative Methods and Network Problems
• Lecture 6 Nonlinear Unconstrained Optimization
• Lecture 7 Nonlinear Objective with Linear Constraints
• Lecture 8 Nonlinear Objective with Nonlinear Constraints
• Lecture 9 Nonlinear Objective with Nonlinear Constraints 2
• Lecture 10 Dynamic Programming
• Lecture 11 Integer Programming
• Lecture 12 Metaheuristics
• Lecture 13 Midterm
• Lecture 14 Game Theory
• Lecture 15 Decision Analysis
• Lecture 16 Markov Chains
• Lecture 17 Queueing Theory
• Lecture 18 Inventory Theory
• Lecture 19 Simulation
• Lecture 20 Review

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