Covers fundamentals of constrained optimization theory for problem-solving in diverse fields
Includes chapters on linear inequalities, convex sets, and separation theorems for a solid foundation
Derives optimality conditions for both differentiable and non-differentiable nonlinear programs
Generalizations to pseudoconvex and quasiconvex functions for broader applicability
Four self-contained appendices on vectors, matrices, topological properties, and differentiable functions for enhanced accessibility
Summarized by Shop
This reprint of the 1969 book of the same name is a concise, rigorous, yet accessible account of the fundamentals of constrained optimization theory. Many problems arising in diverse fields such as machine learning, medicine, chemical engineering, structural design, and airline scheduling can be reduced to a constrained optimization probl
