| EstimationUtils |
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| EstimatorBasicAvg |
Basic average case estimator for matrix sparsity:
sp = 1 - Math.pow(1-sp1*sp2, k)
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| EstimatorBasicWorst |
Basic average case estimator for matrix sparsity:
sp = Math.min(1, sp1 * k) * Math.min(1, sp2 * k).
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| EstimatorBitsetMM |
This estimator implements a naive but rather common approach of boolean matrix
multiplies which allows to infer the exact non-zero structure and thus is
also useful for sparse result preallocation.
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| EstimatorBitsetMM.BitsetMatrix |
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| EstimatorBitsetMM.BitsetMatrix1 |
This class represents a boolean matrix and provides key operations.
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| EstimatorBitsetMM.BitsetMatrix2 |
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| EstimatorDensityMap |
This estimator implements an approach called density maps, as introduced in
David Kernert, Frank Köhler, Wolfgang Lehner: SpMacho - Optimizing Sparse
Linear Algebra Expressions with Probabilistic Density Estimation.
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| EstimatorDensityMap.DensityMap |
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| EstimatorLayeredGraph |
This estimator implements an approach based on a so-called layered graph,
introduced in
Edith Cohen.
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| EstimatorLayeredGraph.LayeredGraph |
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| EstimatorMatrixHistogram |
This estimator implements a remarkably simple yet effective
approach for incorporating structural properties into sparsity
estimation.
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| EstimatorMatrixHistogram.MatrixHistogram |
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| EstimatorSample |
This estimator implements an approach based on row/column sampling
Yongyang Yu, MingJie Tang, Walid G.
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| EstimatorSampleRa |
This estimator implements an approach based on row/column sampling
Rasmus Resen Amossen, Andrea Campagna, Rasmus Pagh:
Better Size Estimation for Sparse Matrix Products.
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| MMNode |
Helper class to represent matrix multiply operators in a DAG
along with references to its abstract data handles.
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| SparsityEstimator |
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