Complete Chapter One
MULTIVARIABLE OPTIMIZATION WITH CONSTRAINTS
DEPARTMENT OF MATHEMATICS
It has been proved that in non linear programming, there are five methods of solving multivariable optimization with constraints.
In this project, the usefulness of some of these methods (Kuhn – Tucker conditions and the Lagrange multipliers) as regards quadratic programming is unveiled.
Also, we found out how the other methods are used in solving constrained optimizations and all these are supported with examples to aid better understanding.
TABLE OF CONTENTS
Title Page i
Approval page ii
Table of Contents iv
1.0 Introduction 1
1.1 Basic definitions 3
1.2 Layout of work 6
2.1 Lagrange Multiplier Method 9
2.2 Kuhn Tucker Conditions 19
2.3 Sufficiency of the Kuhn-Tucker Conditions 24
2.4 Kuhn Tucker Theorems 30
2.5 Definitions – Maximum and minimum of a function 34
2.6 Summary 38
3.1 Newton Raphson Method 39
3.2 Penalty Function 53
3.3 Method of Feasible Directions 57
3.4 Summary 67
4.0 Introduction 68
4.1 Definition – Quadratic Programming 69
4.2 General Quadratic Problems 70
4.3 Methods 75
4.4 Ways/Procedures of Obtaining the optimal
Solution from the Kuhn-Tucker Conditions
The Two-Phase Method 76
The Elimination Method 77
4.5 Summary 117
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