xref: /petsc/src/tao/leastsquares/impls/brgn/brgn.h (revision 8e85b1b3a48c719bb7d116aee5cfc4a5b180d411)
1737f463aSAlp Dener /*
26e9726d0SXiang Huang Context for Bounded Regularized Gauss-Newton algorithm.
36e9726d0SXiang Huang Extended with L1-regularizer with a linear transformation matrix D:
46e9726d0SXiang Huang 0.5*||Ax-b||^2 + lambda*||D*x||_1
56e9726d0SXiang Huang When D is an identity matrix, we have the classic lasso, aka basis pursuit denoising in compressive sensing problem.
6737f463aSAlp Dener */
7737f463aSAlp Dener 
8737f463aSAlp Dener #if !defined(__TAO_BRGN_H)
9737f463aSAlp Dener #define __TAO_BRGN_H
10737f463aSAlp Dener 
11*8e85b1b3SXiang Huang #include <../src/tao/bound/impls/bnk/bnk.h>  /* BNLS, a sub-type of BNK, is used in brgn solver */
12737f463aSAlp Dener 
13737f463aSAlp Dener typedef struct {
14*8e85b1b3SXiang Huang   Mat H, D;  /* Hessian, and Dictionary matrix have size N*N, and K*N respectively. (Jacobian M*N not used here) */
15*8e85b1b3SXiang Huang   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. */
16e1e80dc8SAlp Dener   Tao subsolver, parent;
178ac80d48SXiang Huang   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)*/
18737f463aSAlp Dener } TAO_BRGN;
19737f463aSAlp Dener 
20737f463aSAlp Dener #endif /* if !defined(__TAO_BRGN_H) */
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