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Algorithms on Discrete Near Optimal Partitions. Edition No. 1

  • ID: 1911012
  • January 2009
  • 132 Pages
  • VDM Publishing House
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In practice, many NP-hard combinatorial optimization
problems can be formulated as partitioning problems.
In such a formulation, each component in the
partition is assigned with a numerical objective
value and the objective function is defined as a
function on the numerical values assigned. The
optimization problem is to minimize or maximize
the objective function on all possible partitions
that satisfy certain constraints. A feasible
partition (i.e., a partition that satisfy all the
constraints) with the optimal objective value is
called an optimal partition. A near-optimal partition
is a partition with an objective value close to the
optimal value. In a partitioning problem, by
exploiting the properties of the underlying
domain, one may be able to construct efficient
heuristic algorithms to produce near-optimal
partitions. We present algorithms for applications in
Higher Dimensional Domain Decomposition, Intensity
Modulated Radiation Therapy (IMRT) including
Intensity Modulated Arc Therapy (IMAT)

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Athula, Gunawardena.
Athula D. Gunawardena:BS(EE, U. of Peradeniya, Sri Lanka),
Ph.D.(Mathematics, U. of Wyoming), Ph.D.(Computer Sciences, U. of
Wisconsin-Madison), Associate Professor at U. of
Wisconsin-Whitewater. Robert R. Meyer: Ph.D.(U. of
Wisconsin-Madison), Professor Emeritus of Computer Sciences, U.
of Wisconsin-Madison.

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