cr.sparse.fom.l1rls_jit¶
- cr.sparse.fom.l1rls_jit(A, b, lambda_, 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))¶
Solver for l1 regulated least square problem
- Parameters
A (cr.sparse.lop.Operator) – A linear operator
b (jax.numpy.ndarray) – The measurements \(b \approx A x\)
lambda (float) – The regularization parameter for the l1 term
x0 (jax.numpy.ndarray) – Initial guess for solution vector
options (FomOptions) – Options for configuring the algorithm
- Returns
Solution of the optimization problem
- Return type
The l1 regularized least square problem is defined as:
(1)¶\[\text{minimize} \frac{1}{2} \| A x - b \|_2^2 + \lambda \| x \|_1\]Sometimes, this is also called LASSO in literature.
