cr.sparse.fom.lasso

cr.sparse.fom.lasso(A, b, tau, x0, options=FomOptions(nonneg=False, solver='at', max_iters=1000, tol=1e-08, L0=1.0, Lexact=inf, alpha=0.9, beta=0.5, mu=0, maximize=False, saddle=False))[source]

Solver for LASSO problem

Parameters

  • A (cr.sparse.lop.Operator) – A linear operator

  • b (jax.numpy.ndarray) – The measurements \(b \approx A x\)

  • tau (float) – The radius of the l1-ball constraint

  • x0 (jax.numpy.ndarray) – Initial guess for solution vector

  • options (FomOptions) – Options for configuring the algorithm

Returns

Solution of the optimization problem

Return type

FomState

The LASSO problem is defined as:

(1)\[\begin{split}\begin{aligned} \underset{x}{\text{minimize}} \frac{1}{2} \| \AAA x - b \|_2^2\\ \text{subject to } \| x \|_1 \leq \tau \end{aligned}\end{split}\]