Convex optimization nonlinear analysis

Function Graphs and the Implicit Function Theorem With this lecture we begin our study of nonlinear optimization by examining the geometry of the feasible set

We have already looked at convex feasible sets in Lecture 22, and on polyhedral feasible sets in Lectures 19 and 25

Here we look at a set FˆRndefined by the vanishing of mfunctions h i(x

This series of complementary textbooks cover all aspects of continuous optimization, and its connections with discrete optimization via duality

The two convex optimization books deal primarily with convex, possibly nondifferentiable, problems and rely on convex analysis

The two convex optimization books deal primarily with convex, possibly nondifferentiable, problems and rely on convex analysis

By contrast the nonlinear programming book focuses primarily on analytical and computational methods for possibly nonconvex differentiable problems