Orthogonal matching pursuit

Constructing the solver with dictionary and expected sparsity level:

solver  = spx.pursuit.single.OrthogonalMatchingPursuit(Dict, K)

Using the solver to obtain the sparse representation of one vector:

result = solver.solve(y)

There are several ways of solving the least squares problem which is an intermediate step in the orthogonal matching pursuit algorithm. Some of these are described below.

Using the solver to obtain the sparse representation of one vector with incremental QR decomposition of the subdictionary for the least squares step:

result = solver.solve_qr(y)

Using the solver to obtain the sparse representations of all vectors in the signal matrix Y independently:

result = solver.solve_all(Y)

Using the solver to obtain the sparse representations of all vectors in the signal matrix Y independently using the linsolve method for least squares:

result = solver.solve_all_linsolve(Y)