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License: MIT License
Multiple sampling modes:
So far only perturbative search has been considered
Optionals:
To allow Evolutionary Algorithms on TSP
Implement if feasible and appropriate
This is to add various binary operations on state machines/graphs to combine GLSMs
Allow for diffs in cost function: instead of calculating a cost function itself, calculate difference in cost between two solutions. Might decrease constant computation term, which is important for large instances of TSP specifically
TBC
Add option for simulated annealing routine to stay at the same temperature for multiple iterations.
sequentialStateMachine<Double, BasicSolution<Double>> {
step {
name = "RandomWalk"
strategy = RandomWalkStrategy(true)
}
terminationPredicate = FixedStepsPredicate(10)
}
raises an exception
Exception in thread "main" java.lang.IllegalStateException: Required value was null.
at space.d_lowl.kglsm.sls.StateMachineTransition$Builder.build(StateMachine.kt:36)
at space.d_lowl.kglsm.sls.SequentialStateMachineBuilder.build(SequentialStateMachineBuilder.kt:109)
TSP, Scheduling, Knapsack
Starting with SAT and binary neighbourhood?
Different temperature schedules
Emperical analysis, search space structure. Chapters 4, 5
Add those both as pre-built exploitable classes and as the code in the notebooks
Python and binary apps. Potentially R?
DSL for defining GLSMs outside the code. Potential fallback to JSON/YAML
Current generic signatures are really cumbersome pretty much everywhere. We want as much of automatic inference as possible.
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