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: */


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

 13: typedef struct {
 14:   PetscErrorCode (*regularizerobjandgrad)(Tao,Vec,PetscReal*,Vec,void*);
 15:   PetscErrorCode (*regularizerhessian)(Tao,Vec,Mat,void*);
 16:   void           *reg_obj_ctx;
 17:   void           *reg_hess_ctx;
 18:   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) */
 19:   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. */
 20:   Vec            damping;  /* Optional diagonal damping matrix. */
 21:   Tao            subsolver,parent;
 22:   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)*/
 23:   PetscReal      downhill_lambda_change,uphill_lambda_change; /* With the lm regularizer lambda diag(J^T J),
 24:                                                                  lambda = downhill_lambda_change * lambda on steps that decrease the objective.
 25:                                                                  lambda = uphill_lambda_change * lambda on steps that increase the objective. */
 26:   PetscInt       reg_type;
 27:   PetscBool      mat_explicit;
 28: } TAO_BRGN;

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