This book begins with a concentrated introduction into deterministic global optimization and
moves forward to present new original results from the authors who are well known experts in
the field. Multiextremal continuous problems that have an unknown structure with Lipschitz
objective functions and functions having the first Lipschitz derivatives defined over
hyperintervals are examined. A class of algorithms using several Lipschitz constants is
introduced which has its origins in the DIRECT (DIviding RECTangles) method. This new class is
based on an efficient strategy that is applied for the search domain partitioning. In addition
a survey on derivative free methods and methods using the first derivatives is given for both
one-dimensional and multi-dimensional cases. Non-smooth and smooth minorants and acceleration
techniques that can speed up several classes of global optimization methods with examples of
applications and problems arising in numerical testing of global optimization algorithms are
discussed. Theoretical considerations are illustrated through engineering applications.
Extensive numerical testing of algorithms described in this book stretches the likelihood of
establishing a link between mathematicians and practitioners. The authors conclude by
describing applications and a generator of random classes of test functions with known local
and global minima that is used in more than 40 countries of the world. This title serves as a
starting point for students researchers engineers and other professionals in operations
research management science computer science engineering economics environmental sciences
industrial and applied mathematics to obtain an overview of deterministic global optimization.
