ISM 206 - Fall 2006

EXAM RESCHEDULED FOR 2pm MONDAY DEC 4, in E2 ROOM 475

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.

Approved elective in most SOE majors. Excellent tools for engineers, economists, mathematicians.

Lecture times:

Tuesday and Thursday, 2 – 3:45pm

Social Science 1, Room 161

Instructor:

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

Phone: 831-459-1878

Office: E2 Room 559

Instructor Office Hours:

Thursday 12pm-2pm or by appointment

Handouts

• Syllabus including prerequisites, a schedule of classes and required readings.
• Homework
• Homework 1 due in class Tue, Oct 10
• Homework 2 due in class Tue, Oct 24
• Homework 3 due in class Thu, Nov 9
• Homework 4 due in class Thu, Nov 16
• Homework 5 due in class Tue, Nov 28
• Selected Lecture notes Powerpoint. Updated versions provided throughout quarter
• Lecture 1 Introduction
• Lecture 2 Linear Programming
• Lecture 3 Simplex Method
• Lecture 4 Duality and Sensitivity Analysis
• Lecture notes pdf
1. Old versions are provided, and will be updated as scribes send revised notes.
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
• Review

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