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A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems (Paperback, Softcover reprint of the original 1st ed. 1999)
Loot Price: R5,777
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A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems (Paperback, Softcover reprint of the original 1st ed. 1999)
Series: Nonconvex Optimization and Its Applications, 31
Expected to ship within 10 - 15 working days
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This book deals with the theory and applications of the
Reformulation- Linearization/Convexification Technique (RL T) for
solving nonconvex optimization problems. A unified treatment of
discrete and continuous nonconvex programming problems is presented
using this approach. In essence, the bridge between these two types
of nonconvexities is made via a polynomial representation of
discrete constraints. For example, the binariness on a 0-1 variable
x . can be equivalently J expressed as the polynomial constraint x
. (1-x . ) = 0. The motivation for this book is J J the role of
tight linear/convex programming representations or relaxations in
solving such discrete and continuous nonconvex programming
problems. The principal thrust is to commence with a model that
affords a useful representation and structure, and then to further
strengthen this representation through automatic reformulation and
constraint generation techniques. As mentioned above, the focal
point of this book is the development and application of RL T for
use as an automatic reformulation procedure, and also, to generate
strong valid inequalities. The RLT operates in two phases. In the
Reformulation Phase, certain types of additional implied polynomial
constraints, that include the aforementioned constraints in the
case of binary variables, are appended to the problem. The
resulting problem is subsequently linearized, except that certain
convex constraints are sometimes retained in XV particular special
cases, in the Linearization/Convexijication Phase. This is done via
the definition of suitable new variables to replace each distinct
variable-product term. The higher dimensional representation yields
a linear (or convex) programming relaxation.
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