Actual source code: brgn.h

1: /*
2: Context for Bounded Regularized Gauss-Newton algorithm.
3: Extended with L1-regularizer with a linear transformation matrix D:
4: 0.5*||Ax-b||^2 + lambda*||D*x||_1
5: When D is an identity matrix, we have the classic lasso, aka basis pursuit denoising in compressive sensing problem.
6: */
8: #pragma once
10: #include <../src/tao/bound/impls/bnk/bnk.h>
12: typedef struct {
13: PetscErrorCode (*regularizerobjandgrad)(Tao, Vec, PetscReal *, Vec, void *);
14: PetscErrorCode (*regularizerhessian)(Tao, Vec, Mat, void *);
15: void *reg_obj_ctx;
16: void *reg_hess_ctx;
17: Mat H, Hreg, D; /* Hessian, Hessian for regulization part, and Dictionary matrix have size N*N, and K*N respectively. (Jacobian M*N not used here) */
18: Vec x_old, x_work, r_work, diag, y, y_work; /* x, r=J*x, and y=D*x have size N, M, and K respectively. */
19: Vec damping; /* Optional diagonal damping matrix. */
20: Tao subsolver, parent;
21: PetscReal lambda, epsilon, fc_old; /* lambda is regularizer weight for both L2-norm Gaussian-Newton and L1-norm, ||x||_1 is approximated with sum(sqrt(x.^2+epsilon^2)-epsilon)*/
22: PetscReal downhill_lambda_change, uphill_lambda_change; /* With the lm regularizer lambda diag(J^T J),
23: lambda = downhill_lambda_change * lambda on steps that decrease the objective.
24: lambda = uphill_lambda_change * lambda on steps that increase the objective. */
25: PetscInt reg_type;
26: PetscBool mat_explicit;
27: } TAO_BRGN;