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LSA Course Guide Search Results: UG, GR, Fall 2007, Dept = IOE
 
Page 1 of 1, Results 1 — 7 of 7
Title
Section
Instructor
Term
Credits
Requirements
IOE 265 — Probability and Statistics for Engineers
Section 001, LEC

Instructor: Herrin,Gary D

FA 2007
Credits: 4

Credit Exclusions: No credit granted to those who have completed or are enrolled in STATS 350, 400, 405, or 412, or ECON 404 or 405, or NRE 438 (or ENVIRON 438).

Graphical Representation of Data; Axioms of Probability, Conditioning, Bayes Theorem; Discrete Distributions (Geometric, Binomial, Poisson); Continuous Distributions (Normal Exponential, Weibull), Point and Interval Estimation, Likelihood Functions, Test of Hypotheses for Means, Variances, and Proportions for One and Two Populations.

Enforced Prerequisites: MATH 116 and ENGR 101; (C->)

IOE 466 — Statistical Quality Control
Section 001, LEC

Instructor: Shi,Jianjun

FA 2007
Credits: 3

Design and analysis of procedures for forecasting and control of production processes. Topics include: attribute and variables sampling plans; sequential sampling plans; rectifying control procedures; and charting, smoothing, forecasting, and prediction of discrete time series.

IOE 510 — Linear Programming I
Section 001, LEC

FA 2007
Credits: 3

Background and Goals: A fundamental problem is the allocation of constrained resources such as funds among investment possibilities or personnel among production facilities. Each such problem has as it's goal the maximization of some positive objective such as investment return or the minimization of some negative objective such as cost or risk. Such problems are called Optimization Problems. Linear Programming deals with optimization problems in which both the objective and constraint functions are linear (the word "programming" is historical and means "planning" rather that necessarily computer programming). In practice, such problems involve thousands of decision variables and constraints, so a primary focus is the development and implementation of efficient algorithms. However, the subject also has deep connections with higher-dimensional convex geometry. A recent survey showed that most Fortune 500 companies regularly use linear programming in their decision making. This course will present both the classical and modern approaches to the subject and discuss numerous applications of current interest.

Content: Formulation of problems from the private and public sectors using the mathematical model of linear programming. Development of the simplex algorithm; duality theory and economic interpretations. Postoptimality (sensitivity) analysis; algorithmic complexity; the ellipsoid method; scaling algorithms; applications and interpretations. Introduction to transportation and assignment problems; special purpose algorithms and advanced computational techniques. Students have opportunities to formulate and solve models developed from more complex case studies and use various computer programs.

Alternatives: Cross-listed as IOE 510.

Subsequent Courses: IOE 610 (Linear Programming II) and IOE 611 (Nonlinear Programming)

Advisory Prerequisite: MATH 217, 417, or 419

IOE 511 — Continuous Optimization Methods
Section 001, LEC

Instructor: Saigal,Romesh; homepage

FA 2007
Credits: 3

Content: Survey of continuous optimization problems. Unconstrained optimization problems: unidirectional search techniques, gradient, conjugate direction, quasi-Newtonian methods; introduction to constrained optimization using techniques of unconstrained optimization through penalty transformation, augmented Lagrangians, and others; discussion of computer programs for various algorithms. Alternatives: Cross-listed as IOE 511. Subsequent Courses: This is not a prerequisite for any other course.

Advisory Prerequisite: MATH 217, 417, or 419.

IOE 553 — Financial Engineering Seminar II
Section 001, LEC

Instructor: Keppo,Jussi Samuli; homepage

FA 2007
Credits: 3

Advanced issues in financial engineering: stochastic interest rate modeling and fixed income markets, derivatives trading and arbitrage, international finance, risk management methodologies include in Value-at-Risk and credit risk. Multivariate stochastic calculus methodology in finance: multivariate Ito's lemma, Ito's stochastic integrals, the Feynman-Kac theorem and Girsanov's theorem.


IOE 562 — Reliability
Section 001, LEC

Instructor: Nair,Vijayan N; homepage

FA 2007
Credits: 3

This course will cover important reliability concepts and methodology that arise in modeling, assessing and improving product reliability and in analyzing field and warranty data.

Topics will be selected from the following:

  • Basic Concepts in Reliability

— Component and System Reliability

— Aging

— Hazard Rates and Failure Rates

  • Component Reliability Modeling and Inference

    — Common Models for Component Reliability

    — Analysis of Time-to-Failure Data

* Types of Censoring Schemes

* Nonparametric Techniques

* Graphical and Formal Goodness-of-Fit Tests for Model Selection

* Parametric Techniques

    — Analysis of Degradation Data

  • Reliability, Availability, and Maintainability for Repairable Systems

    — System Structures

    — Common Models for System Reliability

    — Analysis of Time-Between-Failure Data

    — Maintenance and Availability

  • Accelerated Stress Testing for Reliability Assessment
  • Reliability Improvement Through Experimental Design
  • Special Topics: Warranty Data Analysis, Stress-Strength Models, etc.

TEXT: Statistical Methods for Reliability Data by Meeker and Escobar (1998), Wiley.

This will be supplemented by selected material from engineering text books in reliability.

Advisory Prerequisite: STATS 425 and 426 (or IOE 316 and 366).

IOE 623 — Computational Finance
Section 001, LEC

Instructor: Conlon,Joseph G; homepage

FA 2007
Credits: 3

Background and Goals: The field of computational finance is rising rapidly in academic and industry. There is a growing need for students with such skills. This course will fill this demand. Documented computer projects will be required in addition to a final examination. Content: This is a course in computational methods in finance and financial modeling. Particular emphasis will be put on interest rate models and interest rate derivatives. Specific topics include Black-Scholes theory, no-arbitrage and complete markets theory, term structure models, Hull and White models, Heath-Jarrow-Morton models, the stochastic differential equations and martingale approach, multinomial tree and Monte Carlo methods, the partial differential equations approach, finite difference methods. Alternatives: none Subsequent Courses: none

Advisory Prerequisite: MATH,MATH 316 and MATH 425 or 525.

 
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