Sparse optimizations for GaBW sampling #331
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| template <typename update_parameters> | ||
| auto compute_reflection(Point &v, Point &p, update_parameters const& params) const | ||
| -> std::enable_if_t<std::is_same_v<MT, Eigen::SparseMatrix<NT, Eigen::RowMajor>> && !std::is_same_v<update_parameters, int>, void> { // MT must be in RowMajor format | ||
| -> std::enable_if_t<std::is_same_v<MT, Eigen::SparseMatrix<NT, Eigen::RowMajor>> && !std::is_same_v<update_parameters, int>, void> { |
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What is update_parameters here? I do not understand !std::is_same_v<update_parameters could you please explain?
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basically there's another compute_reflection function above which takes an integer (just the facet) as the 3rd argument, and for some reason, if I don't have that condition the compiler decides to call this function assuming that the typename of update_parameters is int. There might maybe be better ways of dealing with these issues, but I remember I tried to solve them for some time and this is the best I could do, I couldn't at all understand how the compiler chooses which function to use when there's multiple ones that match
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It seems that we are too generic here and that complicates the interface a lot! compute_reflection is called by several walks. Instead of creating all those complicated overloads why not simply create a new name. Especially if this is the case that this function is only called by a single walk (i.e. accelerated billiard walk). Moreover, update_parameters should not be a template but the struct defined in billiard walk, in all other cases this code will not compile since all other "update_parameters" does not have a moved_dist field.
| typedef typename Polytope::VT VT; | ||
| typedef typename Point::FT NT; | ||
| using AA_type = std::conditional_t< std::is_same_v<MT, typename Eigen::SparseMatrix<NT, Eigen::RowMajor>>, typename Eigen::SparseMatrix<NT>, DenseMT >; |
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similar comment to AE_type, is there a better naming?
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hmm, I'll think of one, I'm not really sure what name I could give it but I'll see if I can come up with a better name.
| if constexpr (std::is_same<AA_type, Eigen::SparseMatrix<NT>>::value) { | ||
| _AA = (P.get_mat() * P.get_mat().transpose()); | ||
| } else { | ||
| _AA.noalias() = (DenseMT)(P.get_mat() * P.get_mat().transpose()); |
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should we explicitly cast it to DenseMT?
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I'm not sure what you mean, for the optimizations I need it to be in colmajor SparseMatrix format
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the question is: "Where is the problem with _AA.noalias() = P.get_mat() * P.get_mat().transpose(); ?
| E_type _E; | ||
| VT _AEA; | ||
| unsigned int _rho; | ||
| update_parameters _update_parameters; | ||
| typename Point::Coeff _lambdas; | ||
| typename Point::Coeff _Av; | ||
| bool was_reset; | ||
| BoundaryOracleHeap<NT> _distances_set; |
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This is defined in uniform ABW, it should be better if it is defined in a separate file and both walks include it.
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yeah, I was thinking about that too, any suggestions for that file name?
| _distances_set.vec[i].first = ( *(b_data + i) - (*(Ar_data + i)) ) / (*(Av_data + i)); | ||
| } | ||
| // rebuild the heap with the new values of (b - Ar) / Av | ||
| _distances_set.rebuild(_update_parameters.moved_dist); |
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Why not inserting the new values in the heap (in O(logn)) instead of rebuilding (in O(n))?
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here, this happens after we set a new direction, in which case now all the values have changed, so it's quicker to rebuild (O(n)) rather than insert each one (O(nlogn)). I'm not entirely sure if it makes a difference, but I think it does, since afterwards I never do O(nlogn) things, just O(non_zeroes * logn), so this O(nlogn) could be a bottleneck
vfisikop
changed the title
Samples faster when the A matrix of the polytope is sparse.
Very fast when both the A matrix of the polytope and the covariance matrix are sparse.