# TAOBRGN
Bounded Regularized Gauss-Newton method for solving nonlinear least-squares problems with bound constraints. This algorithm is a thin wrapper around TAOBNTL that constructs the Gauss-Newton problem with the user-provided least-squares residual and Jacobian. The algorithm offers an L2-norm ("l2pure"), L2-norm proximal point ("l2prox") regularizer, and L1-norm dictionary regularizer ("l1dict"), where we approximate the L1-norm ||x||_1 by sum_i(sqrt(x_i^2+epsilon^2)-epsilon) with a small positive number epsilon. Also offered is the "lm" regularizer which uses a scaled diagonal of J^T J. With the "lm" regularizer, BRGN is a Levenberg-Marquardt optimizer. The user can also provide own regularization function. 
## Options Database Keys

- ***-tao_brgn_regularization_type -*** regularization type ("user", "l2prox", "l2pure", "l1dict", "lm") (default "l2prox")
- ***-tao_brgn_regularizer_weight  -*** regularizer weight (default 1e-4)
- ***-tao_brgn_l1_smooth_epsilon   -*** L1-norm smooth approximation parameter: ||x||_1 = sum(sqrt(x.^2+epsilon^2)-epsilon) (default 1e-6)




## Level
beginner

## Location
<A HREF="PETSC_DOC_OUT_ROOT_PLACEHOLDER/src/tao/leastsquares/impls/brgn/brgn.c.html#TAOBRGN">src/tao/leastsquares/impls/brgn/brgn.c</A>

## Examples
<A HREF="PETSC_DOC_OUT_ROOT_PLACEHOLDER/src/tao/leastsquares/tutorials/cs1.c.html">src/tao/leastsquares/tutorials/cs1.c.html</A><BR>
<A HREF="PETSC_DOC_OUT_ROOT_PLACEHOLDER/src/tao/leastsquares/tutorials/tomography.c.html">src/tao/leastsquares/tutorials/tomography.c.html</A><BR>


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[Edit on GitLab](https://gitlab.com/petsc/petsc/-/edit/release/src/tao/leastsquares/impls/brgn/brgn.c)


[Index of all Tao routines](index.md)  
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