This book presents several algorithms for solving unconstrained and constrained nonlinear programming problems. The book is a mathematical treatment of optimization methods. It is not applications oriented.

The book has seven chapters. I will briefly summarize each one to illustrate the book’s coverage. Chapter 1 is an introductory treatment of necessary and sufficient conditions for minimizing functions. Chapter 2 discusses convergence theorems for iterative processes. This is an excellent treatment and is a highlight of the book. Chapter 3 discusses the penalty function method of handling constraints. Chapter 4 shows how to solve nonlinear programming problems via the Lagrangian approach. Chapter 5 discusses the reduced gradient and gradient projection methods. Chapter 6 transforms the optimal control problem into a nonlinear programming problem. This results in a high dimension problem with its associated difficulties. Chapter 7 presents an approach for finding global solutions to minimization problems.

There are very few examples in the book (mostly in Chapter 6). The salient parts of the book are Chapters 2, 6, and 7. If you like a heavy mathematical treatment of optimization methods, you may be interested in this book.