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Matching Pursuit

Matching pursuit and related greedy algorithms iteratively select sources that best explain the residual signal. They produce sparse solutions and can handle correlated sources effectively.

This category contains 10 solvers.

Solvers

Full Name Solver ID Description
Compressive Sampling Matching Pursuit CoSaMP Greedy sparse recovery algorithm for single-measurement vectors. Iteratively identifies a support set and solves a le...
Iterative Subspace Smooth Matching Pursuit ISubSMP Smooth, subspace-based matching pursuit variant that operates on a spatially smoothed (patch) dictionary and iterates...
Orthogonal Matching Pursuit OMP Greedy sparse recovery that iteratively selects the best-correlating atom and re-solves a least-squares fit on the se...
Reduce Multi-Measurement-Vector and Boost ReMBo Randomly reduces a multi-measurement problem to repeated single-measurement OMP-style recovery, then boosts by re-fit...
Smooth Matching Pursuit SMP Matching pursuit variant that selects spatial patches (and optionally singletons) using a smoothed dictionary built f...
Simultaneous Orthogonal Matching Pursuit SOMP Multi-measurement-vector extension of OMP that selects atoms jointly across time/conditions by aggregating per-atom c...
Subspace Pursuit SP Greedy sparse recovery that alternates between support expansion and pruning, solving a least-squares fit on the cand...
Smooth Simultaneous Matching Pursuit SSMP Multi-measurement smooth matching pursuit that selects spatial patches using a smoothed (adjacency-based) dictionary ...
Simultaneous Subspace Pursuit SSP Multi-measurement-vector extension of Subspace Pursuit that selects and prunes a joint support using aggregated (acro...
Subspace Smooth Matching Pursuit SubSMP Smooth matching pursuit variant that projects data into a low-dimensional subspace and merges patch supports across c...