This book is designed for a first course in nonlinear optimization.
It starts with classical optimization notions from calculus and
proceeds smoothly to a study of convex functions. This is followed
by material on basic numerical methods, least squares,
Karush-Kuhn-Tucker theory, penalty functions, and Lagrange
multipliers. The book has been tested in the classroom; the
approach is rigorous at all times and geometric intuition is
developed. The numerical methods are up-to-date. The presentation
emphasizes the mathematical ideas behind computer codes.The book is
aimed at the student who has a working knowledge of linear algebra
and partial differentiation but has had no previous exposure to
optimization. Mathematics instructors will be comfortable with the
mathematical approach which deemphasizes recipes and emphasizes
understanding underlying concepts. There are many exercises chosen
to highlight the fundamental ideas.
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