cr.sparse.fom.lasso_jit¶
- cr.sparse.fom.lasso_jit(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))¶
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
The LASSO problem is defined as:
(1)¶\[\begin{split}\begin{aligned} \underset{x}{\text{minimize}} \frac{1}{2} \| \AAA x - b \|_2^2\\ \text{subject to } \| x \|_1 \leq \tau \end{aligned}\end{split}\]
