Implement UpdaterCovarAdaptation::setSubMatrices to mimic parallel optimization

[stulp]

The parallel optimization is no longer needed if the covariance matrix updates updates the matrix in submatrices. The simple solution to this would be to have a function UpdaterCovarAdaptation::setSubMatrices(VectorXi), in which the sizes of the submatrices are set, rather than derived from the array of distributions:

dmpbbo/src/bbo/runOptimization.cpp

Line 112 in ca900e3

covar_block_sizes[pp] = distributions[pp].mean().size();

The following functions can then be deleted:

dmpbbo/src/dmp_bbo/runOptimizationTask.cpp

Line 227 in ca900e3

/** \todo Get rid of runOptimizationParallelDeprecated(), and implement in UpdaterCovarAdapation

dmpbbo/src/bbo/runOptimization.cpp

Line 65 in ca900e3

bool saveToDirectory(

[stulp]

See #58 (comment) for backgroun information.

Current implementation

runOptimizationParallelDeprecated() has a list of N seperate distributions with MxM covariance matrices. They are each sampled separately, and the results concatenated into one NxM parameter vector.

Future (simpler implementation)

Have one NM x NM covariance matrix, but organize it in blocks.

• Advantage: simpler code, because running the black-box optimization part is the same for all (a* *voids code redundancy)
• Disadvantage. NM x NM with many 0s takes up more memory than N times MxM. However, since there is only one copy of this distribution, I think the advantages outweighs the disadvantage (clearer non-redundant code preferred over complete memory optimization).

Implementation Options:

• Option 1: Have a UpdaterCovarAdaptationBlocks (which inhertits from UpdaterCovarAdaptation?) and updates block-by-block.
• Option 2: Pass blocks to constructor. Treat differently from normal updater.

[pranshumalik14]

This notebook provides yet another way to have compact code following the current implementation.

[stulp]

Yes, very nice! See also my comments in #58