/*
Context for Bounded Regularized Gauss-Newton algorithm.
Extended with L1-regularizer with a linear transformation matrix D:
0.5*||Ax-b||^2 + lambda*||D*x||_1
When D is an identity matrix, we have the classic lasso, aka basis pursuit denoising in compressive sensing problem.
*/

#if !defined(__TAO_BRGN_H)
#define __TAO_BRGN_H

#include <../src/tao/bound/impls/bnk/bnk.h>

typedef struct {
  Mat J, H, D;  /* Jacobian, Hessian, and Dictionary matrix have size M*N, N*N, and P*N respectively. */
  Vec x_old, x_work, r_work, diag, y, y_work;  /* x, r=J*x, and y=D*x have size N, M, and P respectively. */
  Tao subsolver, parent;
  PetscReal lambda, epsilon; /* 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)*/
} TAO_BRGN;

#endif /* if !defined(__TAO_BRGN_H) */