A library of numerical and combinatorial optimization procedures written in OCaml, with additional bindings for external optimization routines.
OCaml Numerical Procedures -
- Simplex Method
- Subplex Method
- Brents Method
- [/] Multi-Dimensional Brents Method
- BFGS Method
- Conjugate Gradient Method
OCaml Combinatorial Procedures -
- Combinators for composing a search procedure
- Simulated Annealing
- Variable Neighborhood Search
- Tabu Search
- Genetic Algorithms
External Bindings -
- GSL
- ???
The search framework will require thoughtfulness to be able to encompass a wide range of known search heuristics. Since the motivation here is for phylogenetics, we are dealing with two NP-Hard problems. We can look to current meta-heuristic literature to design a general library. Most of these algorithms are dependent on a local search. At minimum this requires,
- Solution - An instance to a Problem
- Neighborhood- A way to generate solutions using local modifications.
- Choose - A method to choose a single member of a neighborhood for successive neighborhood searches.
Full search procedures, like branch and bound and depth first search will also have to be employed as well to round out an exhaustive approach to search on small data-sets. It is currently under consideration that the neighborhood be generated from a lazy-list. In this way, we believe a wide range of options and strategies can be employed from combining small fragments that embody a search plan.
A local-search procedure can be used to build up a more global search procedure that includes perturbations, a more robust tabu-search, and other functionalities for global optimization and meta-heuristics. These procedures can be defined separately instead of a single all encompassing search function, and be parameterized about the specific requirements of the topology. In general meta-heuristics can be separated into two categories:
Iterative Methods
- Simulated Annealing (with restart)
- Tabu-Search
- Greedy Randomized Adaptive Search Procedure (GRASP)
- Variable Neighborhood Search
- Guided Local Search
- Iterated Local Search
Population Based Methods
- Scatter Search / Path Relinking
- Evolutionary Computation (eg, GA)
- Ant Colony Optimization
- Firefly Optimization
The potential to compose these methods into hyper-heuristics is still a question, but at the very least these methods encompass a wide range of ways to vary the degrees of Intensification and Diversification. Some questions remain regarding the search procedures,
- Will this design be robust enough for parallel computation?
- Does the first-class module cause speed/performance issues?
- Can neighborhoods be partitioned effectively with the scheme we have?
- neighborhoods as lazy-lists allow for filtering, look-ahead, tabu, and convergence options easily.
- neighborhoods are composed of deltas of a candidate solution and not solutions themselves. This allows the problems solution itself to be a difficult problem and heuristics employed between testing a solution and accepting it as better.
Testing is being done through OUnit. See the test/ directory for information.
Documentation can be built through make docs command. References are included
here from the source.
- Nicholas Lucaroni (nlucaroni at amnh dot org)
- http://github.com/AMNH/ocamion