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 */ 7 8 #if !defined(__TAO_BRGN_H) 9 #define __TAO_BRGN_H 10 11 #include <../src/tao/bound/impls/bnk/bnk.h> /* BNLS, a sub-type of BNK, is used in brgn solver */ 12 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 Tao subsolver,parent; 21 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)*/ 22 PetscInt reg_type; 23 } TAO_BRGN; 24 25 #endif /* if !defined(__TAO_BRGN_H) */ 26