Results 1  10
of
2,474,656
Parallel Processing of Discrete Optimization Problems
 IN ENCYCLOPEDIA OF MICROCOMPUTERS
, 1993
"... Discrete optimization problems (DOPs) arise in various applications such as planning, scheduling, computer aided design, robotics, game playing and constraint directed reasoning. Often, a DOP is formulated in terms of finding a (minimum cost) solution path in a graph from an initial node to a goa ..."
Abstract

Cited by 21 (6 self)
 Add to MetaCart
Discrete optimization problems (DOPs) arise in various applications such as planning, scheduling, computer aided design, robotics, game playing and constraint directed reasoning. Often, a DOP is formulated in terms of finding a (minimum cost) solution path in a graph from an initial node to a
DIMACS Workshop on Parallel Processing of Discrete Optimization Problems
, 1994
"... Introduction Discrete optimization problems (DOPs) arise in various applications such as planning, scheduling, computer aided design, robotics, game playing and constraint directed reasoning. Often, a DOP is formulated in terms of finding a (least cost) solution path in a graph from an initial node ..."
Abstract
 Add to MetaCart
Introduction Discrete optimization problems (DOPs) arise in various applications such as planning, scheduling, computer aided design, robotics, game playing and constraint directed reasoning. Often, a DOP is formulated in terms of finding a (least cost) solution path in a graph from an initial node
Discrete Optimization Problems with Random Cost Elements
"... In a general class of discrete optimization problems, some of the elements may have random costs associated with them. In such a situation, the notion of optimality needs to be suitably modified. In this work we define an optimal solution to be a feasible solution with the minimum risk. We focus o ..."
Abstract
 Add to MetaCart
In a general class of discrete optimization problems, some of the elements may have random costs associated with them. In such a situation, the notion of optimality needs to be suitably modified. In this work we define an optimal solution to be a feasible solution with the minimum risk. We focus
Simultaneous Generalized Hill Climbing Algorithms for Addressing Sets of Discrete Optimization Problems
, 2000
"... Generalized hill climbing (GHC) algorithms provide a framework for using local search algorithms to address intractable discrete optimization problems. Many wellknown local search algorithms can be formulated as GHC algorithms, including simulated annealing, threshold accepting, Monte Carlo search, ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Generalized hill climbing (GHC) algorithms provide a framework for using local search algorithms to address intractable discrete optimization problems. Many wellknown local search algorithms can be formulated as GHC algorithms, including simulated annealing, threshold accepting, Monte Carlo search
StateoftheArt in Parallel Search Techniques for Discrete Optimization Problems
"... Discrete optimization problems arise in a variety of domains such as VLSI design, transportation, scheduling and management, and design optimization. Very often, these problems are solved using state space search techniques. Due to the high computational requirements and inherent parallel nature o ..."
Abstract
 Add to MetaCart
Discrete optimization problems arise in a variety of domains such as VLSI design, transportation, scheduling and management, and design optimization. Very often, these problems are solved using state space search techniques. Due to the high computational requirements and inherent parallel nature
ant colony algorithms applied to discrete Optimization problems
"... Five variants of the ant colony optimization metaheuristic, namely Ant System, Ant Colony System, MaxMin Ant System, Rank Based Ant System, and BestWorst Ant System, were implemented and applied to discrete function as well as structural optimization problems in order to compare the dierent impl ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Five variants of the ant colony optimization metaheuristic, namely Ant System, Ant Colony System, MaxMin Ant System, Rank Based Ant System, and BestWorst Ant System, were implemented and applied to discrete function as well as structural optimization problems in order to compare the dierent
Discrete Optimization Problems for Radiation Therapy Planning
"... Abstract: A wellstudied problem in intensity modulated radiation therapy (IMRT) is the representation of a given intensity matrix, i.e. a matrix of nonnegative integers, as a nonnegative linear combination of special 01matrices, called segments. These segments can be practically realized by multi ..."
Abstract
 Add to MetaCart
Abstract: A wellstudied problem in intensity modulated radiation therapy (IMRT) is the representation of a given intensity matrix, i.e. a matrix of nonnegative integers, as a nonnegative linear combination of special 01matrices, called segments. These segments can be practically realized
Some Discrete Optimization Problems With Secondary Storage Applications
"... Then we can define d(i,j) = m=(,), if objects i,j are located at points ,. e is a parameter depending on the application. For example, == corresponds to the case of a recent archive storage where accessing is done by an XY mechanism [23]. We consider the case ==2 first. This corresponds to the ..."
Abstract
 Add to MetaCart
to the Euclidean distance function. We note that the optimal solution depends on the values of p rather than on their rankings alone as in the 1dimensional case. The following example illustrates this fact. ]P5 IP4 (pl,P2,P3,P4) = (0.33,0.32,0.31,0.04) (pl,P2,P3,P4) = (0
Robustness and efficiency: A study of the relationship and an algorithm for the bicriteria discrete optimization problem
, 2002
"... We study various definitions of robustness in a discrete scenario discrete optimization setting. We show that a generalized definition of robustness into which scenario weights are introduced can be used to identify the efficient solutions of multiple objective discrete optimization problems. We sho ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
We study various definitions of robustness in a discrete scenario discrete optimization setting. We show that a generalized definition of robustness into which scenario weights are introduced can be used to identify the efficient solutions of multiple objective discrete optimization problems. We
Results 1  10
of
2,474,656