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> 12 13 typedef struct { 14 Mat J, H, D; /* Jacobian, Hessian, and Dictionary matrix have size M*N, N*N, and P*N respectively. */ 15 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. */ 16 Tao subsolver, parent; 17 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)*/ 18 } TAO_BRGN; 19 20 #endif /* if !defined(__TAO_BRGN_H) */ 21