Introduction to Global Optimization Exploiting Space-Filling Curves provides an overview of
classical and new results pertaining to the usage of space-filling curves in global
optimization. The authors look at a family of derivative-free numerical algorithms applying
space-filling curves to reduce the dimensionality of the global optimization problem along
with a number of unconventional ideas such as adaptive strategies for estimating Lipschitz
constant balancing global and local information to accelerate the search. Convergence
conditions of the described algorithms are studied in depth and theoretical considerations are
illustrated through numerical examples. This work also contains a code for implementing
space-filling curves that can be used for constructing new global optimization algorithms.
Basic ideas from this text can be applied to a number of problems including problems with
multiextremal and partially defined constraints and non-redundant parallel computations can be
organized. Professors students researchers engineers and other professionals in the fields
of pure mathematics nonlinear sciences studying fractals operations research management
science industrial and applied mathematics computer science engineering economics and the
environmental sciences will find this title useful .
