In this paper it is argued that continuing advances in computing power present both a challenge and an opportunity to researchers in the social sciences. There are opportunities in at least two areas in the generation of more sophisticated theories about spatial problems: through techniques such as data mining, pattern recognition, and neural network models; and through the application of methods which were previously too difficult to test and utilise. It is the latter challenge which is primarily addressed in this paper. The authors introduce an important problem of network optimisation and discuss the methods by which the problem may be solved. They show how existing solution procedures are hampered by the computational complexity of the ...
The problem of transmission line corridor location can be considered, at best, a "wicked" public sys...
In this paper we focus on the parallel computation of large- scale equilibrium and op-timization pro...
We describe the use of neural networks for optimization and inference associated with a variety of c...
There are a number of large networks which occur in many problems dealing with the flow of power, co...
The paper demonstrates some of the benefits that high performance computing has to offer geographers...
Most parallel processing methods developed for geographic analyses bind the design of domain decompo...
Caption title.Includes bibliographical references (p. 82-95).Supported by the NSF. CCR-9103804Dimitr...
Abstract—Problems arising in different areas such as numerical methods, simulation or optimization c...
The p-Median problem (PMP) is one of the most widely applied location problems in urban and regional...
Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes...
Abstract: This study examines the value of utilizing neural net modeling for issues relating to opti...
Three optimization methods derived from natural sciences are considered for allocating data to multi...
After more than a decade of research, there now exist several neural-network techniques for solving ...
Problems arising in different areas such as numerical methods, simulation or optimization can be eff...
The paper presents a novel method for monitoring network optimisation, based on a recent machine lea...
The problem of transmission line corridor location can be considered, at best, a "wicked" public sys...
In this paper we focus on the parallel computation of large- scale equilibrium and op-timization pro...
We describe the use of neural networks for optimization and inference associated with a variety of c...
There are a number of large networks which occur in many problems dealing with the flow of power, co...
The paper demonstrates some of the benefits that high performance computing has to offer geographers...
Most parallel processing methods developed for geographic analyses bind the design of domain decompo...
Caption title.Includes bibliographical references (p. 82-95).Supported by the NSF. CCR-9103804Dimitr...
Abstract—Problems arising in different areas such as numerical methods, simulation or optimization c...
The p-Median problem (PMP) is one of the most widely applied location problems in urban and regional...
Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes...
Abstract: This study examines the value of utilizing neural net modeling for issues relating to opti...
Three optimization methods derived from natural sciences are considered for allocating data to multi...
After more than a decade of research, there now exist several neural-network techniques for solving ...
Problems arising in different areas such as numerical methods, simulation or optimization can be eff...
The paper presents a novel method for monitoring network optimisation, based on a recent machine lea...
The problem of transmission line corridor location can be considered, at best, a "wicked" public sys...
In this paper we focus on the parallel computation of large- scale equilibrium and op-timization pro...
We describe the use of neural networks for optimization and inference associated with a variety of c...