King Fahd University of Petroleum & Minerals

Department of Mathematical Sciences

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MATH 480 -   Linear & Nonlinear Programming  (3-0-3)

Syllabus FINAL GRADS

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Syllabus

Catalog Description

                                                        

Formulation of linear programs.  Basic properties of linear programs.  The simplex method. Duality. Necessary and sufficient conditions for unconstrained problems.  Minimization of convex functions. A method of solving unconstrained problems.  Equality and inequality constrained optimization.  The Lagrange multipliers theorem.  The Kuhn-Tucker conditions.  A method of solving constrained problems.

Prerequisite:  Math 280; ICS 101, ICS 102, or ICS 103

Objectives

The course deals with the basic ideas of mathematical programming (linear and nonlinear). We shall see how simple mathematics plays a significant role in the development of these ideas. The students will be asked to work out the computational implementation of a numerical algorithm for solving Linear and Nonlinear Program­ming problems and do presentations.

Textbook:  Linear and Nonlinear Programming by E.G. Luenberger, 2nd  edition (1994).

Grading Policy:    First Majaor 15 points, Second Major 15 points, Homework 5 points,  Project 15 points, Final 40 points.

 

Week #

Sections

Topics

1

2.1-2.3

Introduction, Examples of Linear Programming Problems, Basic Solutions

2

2.4-2.5

The Fundamental Theorem of Linear Programming, Relations to Convexity

3

3.1-3.5

Pivots, Adjacent Extreme Points, Determining a Minimum Feasible Solution, Computational Procedure~Simplex Method, Artificial Variables

4

3.7-3.8

Matrix Form of the Simplex Method, The Revised Simplex Method

5

4.1-4.2

Dual Linear Programs, The Duality Theorem

6

4.3-4.5

Relations to the Simplex Procedure, Sensitivity and Complementary Slackness, The Dual Simplex Method

7

5.1-5.4

Transportation Problem

8

6.1-6.4

First Order Necessary Conditions, Examples of Unconstarined Problems, Second-Order Conditions, Convex and Concave Functions,

9

6.5, 7.8

Minimization and Maximization of Convex Functions, Newton’s Method

10

9.1-9.4

Modified Newton’s Method, Construction of the Inverse, Davidon-Fletcher-Powell Method, The Broyden Family

11

10.1-10.3

Constraints, Tangent Plane, First-Order Necessary Conditions

12

 

 

13

10.5-10.6, 10.8

Second-Order Conditions, Eigenvalues in Tangent Subspace, Inequality Constraints

14

12.1-12.3

Penalty Methods, Barrier Methods,  Properties of Penalty and Barrier_Functions

15

14.1-14.2,14.4

Quadratic Programming, Direct Methods, Modified Newton’s Methods