CR.Sparse
v0.1.6
Contents:
Introduction
Tutorials
Getting Started
Fourier and Wavelet Representations
CR.Sparse API Documentation
Digital Signal Processing
Wavelets
Sparsifying Dictionaries and Sensing Matrices
Linear Operators
Greedy Sparse Recovery/Approximation Algorithms
Basic Matching Pursuit Based Algorithms
cr.sparse.pursuit.omp.solve
cr.sparse.pursuit.omp.matrix_solve
cr.sparse.pursuit.omp.matrix_solve_jit
cr.sparse.pursuit.omp.matrix_solve_multi
cr.sparse.pursuit.cosamp.solve
cr.sparse.pursuit.cosamp.matrix_solve
cr.sparse.pursuit.cosamp.matrix_solve_jit
cr.sparse.pursuit.cosamp.operator_solve
cr.sparse.pursuit.cosamp.operator_solve_jit
cr.sparse.pursuit.sp.solve
cr.sparse.pursuit.sp.matrix_solve
cr.sparse.pursuit.sp.matrix_solve_jit
cr.sparse.pursuit.sp.operator_solve
cr.sparse.pursuit.sp.operator_solve_jit
Hard Thresholding Based Algorithms
Data Types
Utilities
Using the greedy algorithms
Convex Optimization based Sparse Recovery/Approximation Algorithms
Sample Data Generation Utilities
Utilities in cr.sparse module
Linear Algebra Subroutines
Numerical Optimization Routines
Evaluation Framework
Examples Gallery
Benchmarks
Acronyms
References
CR.Sparse
»
CR.Sparse API Documentation
»
Greedy Sparse Recovery/Approximation Algorithms
»
cr.sparse.pursuit.sp.matrix_solve_jit
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cr.sparse.pursuit.sp.matrix_solve_jit
¶
cr.sparse.pursuit.sp.
matrix_solve_jit
(
Phi
,
y
,
K
,
max_iters
=
None
,
res_norm_rtol
=
0.0001
)
¶
Solves the sparse recovery problem
\(y = \Phi x + e\)
using Subspace Pursuit for matrices
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v: v0.1.6
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