# Troubleshooting Failed Solves

If your solve does not converge, i.e. you exceed the maximum number of nonlinear iterations (max_nl_its), the time step gets cut. If this occurs, you will eventually reach the minimum time step and the solve will fail:


Time Step  1, time = 125
dt = 2e-8
0 Nonlinear |R| = 6.202666e+03
1 Nonlinear |R| = 9.602686e+02
2 Nonlinear |R| = 7.814158e+02
3 Nonlinear |R| = 5.736782e+02
4 Nonlinear |R| = 4.872949e+02
5 Nonlinear |R| = 4.420177e+02
6 Nonlinear |R| = 4.024829e+02
7 Nonlinear |R| = 3.664817e+02
8 Nonlinear |R| = 3.341195e+02
9 Nonlinear |R| = 3.052765e+02
10 Nonlinear |R| = 2.795248e+02
Solve Did NOT Converge!

Time Step  1, time = 100
dt = 1e-8
0 Nonlinear |R| = 6.202666e+03
1 Nonlinear |R| = 9.600767e+02
2 Nonlinear |R| = 7.746469e+02
3 Nonlinear |R| = 5.609970e+02
4 Nonlinear |R| = 4.766359e+02
5 Nonlinear |R| = 4.323344e+02
6 Nonlinear |R| = 3.935847e+02
7 Nonlinear |R| = 3.584233e+02
8 Nonlinear |R| = 3.267590e+02
9 Nonlinear |R| = 2.985230e+02
10 Nonlinear |R| = 2.733081e+02
Solve Did NOT Converge!

*** ERROR ***
Solve failed and timestep already at or below dtmin, cannot continue!


Your solve may be failing for various reasons:

## Small initial tolerance

If you are running a simulation in which for a specific time step the initial tolerance begins very small (>1e-6), your solve fails simply because the nl_rel_tol would force te residual too small to reach.


Initial residual before setting preset BCs: 4.84302e-09
0 Nonlinear |R| = 4.843024e-09
1 Nonlinear |R| = 1.273033e-13
2 Nonlinear |R| = 5.226870e-17
3 Nonlinear |R| = 2.580131e-17
4 Nonlinear |R| = 2.522566e-17
5 Nonlinear |R| = 2.522556e-17
Solve Did NOT Converge!


You may be in a close to steady-state regime such that the previous solution is very close to the current solution. In this case, setting a nl_abs_tol will fix your problem.

## Bad linear convergence

If for a specific time step your linear iterations are not dropping such that it takes many linear iterations to reach l_tol or you may never reach l_tol because you hit the l_max_hit, your preconditioner is not working for the problem.


Initial residual before setting preset BCs: 65444.1
0 Nonlinear |R| = 6.544408e+04
0 Linear |R| = 6.544408e+04
1 Linear |R| = 5.381557e+04
2 Linear |R| = 5.381315e+04
3 Linear |R| = 5.381315e+04
4 Linear |R| = 5.381315e+04
5 Linear |R| = 5.381315e+04
6 Linear |R| = 5.381315e+04
7 Linear |R| = 5.381315e+04
8 Linear |R| = 5.381315e+04
9 Linear |R| = 5.381315e+04
10 Linear |R| = 5.381315e+04
11 Linear |R| = 5.381315e+04
12 Linear |R| = 5.381315e+04
13 Linear |R| = 5.381315e+04
14 Linear |R| = 5.381315e+04
15 Linear |R| = 5.381315e+04
1 Nonlinear |R| = 5.510740e+04
0 Linear |R| = 5.510740e+04
1 Linear |R| = 5.510740e+04
2 Linear |R| = 5.510738e+04
3 Linear |R| = 5.510737e+04
4 Linear |R| = 5.510735e+04
5 Linear |R| = 5.510734e+04
6 Linear |R| = 5.510732e+04
7 Linear |R| = 5.510730e+04
8 Linear |R| = 5.510729e+04
9 Linear |R| = 5.510727e+04
10 Linear |R| = 5.510726e+04
11 Linear |R| = 5.510724e+04
12 Linear |R| = 5.510722e+04
13 Linear |R| = 5.510721e+04
14 Linear |R| = 5.510719e+04
15 Linear |R| = 5.510718e+04
Solve Did NOT Converge!


In this case you are likely to require many nonlinear iterations as well, but the reason is that your linear iterations don't drop. This could be due to missing terms or errors in your Jacobian or because the way you are applying your preconditioner in PETSc is not good for the problem. Make sure your Jacobian is correct and add off-diagonal terms for multivariable problems.

An additional possible reason for a poor linear solve is that your problem is very poorly conditioned. You can inspect the condition number of your matrix for small problems (should be less than 1000 degrees of freedom) by running with the PETSc options -pc_type svd -pc_svd_monitor. These options will tell you your condition number as well as how many singular values you have. If you have any singular values, then you may have omitted a boundary condition, you may have a null space (all Neumann boundary conditions for example), or you may have very poor scaling between variables in a multi-physics simulation. You may even have run into issues if you have nodal boundary conditions (which introduce values of unity on the diagonals) and the Jacobian entries from your physics (kernels) are very large. You want your condition number to be as close to unity as possible. To address the latter problem or poor relative scaling between variables, you can use MOOSE's automatic scaling feature which will bring different physics Jacobians as close to unity as possible. To turn on this feature, set the automatic_scaling parameter in the Executioner block to true. Additionally, if you want to update scaling factors at every time step then set Executioner/compute_scaling_once=false. By default this latter parameter is set to true in order to save computational expense.

## Bad nonlinear convergence

If your linear iterations are dropping fine but it takes lots of nonlinear iterations, then your problem is very nonlinear and it is just hard to solve. In this case, you should decrease the time step. However, if you have a multivariable problem, the two residuals may have very different magnitudes, which will make the system hard to solve. Print the nonlinear residuals using the debug block to check their relative magnitudes at the end of a solve. If they are more than an order of magnitude off, then use the scaling parameter in the variables block to scale the smaller variable up.