Difference between revisions of "Running ParSA"
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| Line 8: | Line 8: | ||
The parameters used for this run are as follows: | The parameters used for this run are as follows: | ||
| − | *Alpha | + | |
| − | + | { | |
| − | + | SA_Solver SA_MIRSolver | |
| − | + | { | |
| − | + | SA_Solver | |
| − | + | { | |
| + | datafilename data.ser | ||
| + | outputfilename parsaoutput | ||
| + | solutionfilename * | ||
| + | overwritesolution better | ||
| + | writefunction path | ||
| + | verbose on | ||
| + | verboselevel 10 | ||
| + | StartSolution Init | ||
| + | } | ||
| + | SA_Scheduler SA_MIRScheduler | ||
| + | { | ||
| + | SA_Scheduler | ||
| + | { | ||
| + | picturedirectory . | ||
| + | OptType MIN | ||
| + | thresholdvalue 0 | ||
| + | initialtemperature -1.0 | ||
| + | initaccratio 0.9 | ||
| + | verboselevel 10 | ||
| + | timelimit 100000 | ||
| + | } | ||
| + | Alpha 0.5 | ||
| + | Betta 2 | ||
| + | endtemperature 0 | ||
| + | Temperature_Reset 0 | ||
| + | } | ||
| + | Betta_Runtime 1.1 | ||
| + | Ebest 0 | ||
| + | Epsilon 0.01 | ||
| + | Maximum_RunLength RunFactor*GetLocalN() | ||
| + | Minimum_RunLength GetLocalN() | ||
| + | Samples 10 | ||
| + | RunFactor 5 | ||
| + | algorithm MIR | ||
| + | } | ||
| + | } | ||
Revision as of 18:47, 30 January 2008
This page will serve as a progress report for the weekly work done on running the ParSA algorithm. Plots of cost function versus step number and the parameters used will be included for each completed run.
Week of 21 January 2008
The goal of the first run was to ensure that the MIR scheduler and solver would be able to successfully complete an entire run.
The parameters used for this run are as follows:
{
SA_Solver SA_MIRSolver
{
SA_Solver
{
datafilename data.ser
outputfilename parsaoutput
solutionfilename *
overwritesolution better
writefunction path
verbose on
verboselevel 10
StartSolution Init
}
SA_Scheduler SA_MIRScheduler
{
SA_Scheduler
{
picturedirectory .
OptType MIN
thresholdvalue 0
initialtemperature -1.0
initaccratio 0.9
verboselevel 10
timelimit 100000
}
Alpha 0.5
Betta 2
endtemperature 0
Temperature_Reset 0
}
Betta_Runtime 1.1
Ebest 0
Epsilon 0.01
Maximum_RunLength RunFactor*GetLocalN()
Minimum_RunLength GetLocalN()
Samples 10
RunFactor 5
algorithm MIR
}
}