[petsc-users] how to stop SNES linesearch (l^2 minimization) from choosing obviously suboptimal lambda?
Andrew McRae
A.T.T.McRae at bath.ac.uk
Wed Jan 25 13:13:03 CST 2017
I have a nonlinear problem in which the line search procedure is making
'obviously wrong' choices for lambda. My nonlinear solver options (going
via petsc4py) include {"snes_linesearch_type": "l2",
"snes_linesearch_max_it": 3}.
After monotonically decreasing the residual by about 4 orders of magnitude,
I get the following...
15 SNES Function norm 9.211230243067e-06
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.13039e-05,
3.14838e-05, 9.21123e-06]
Line search: lambdas = [1.25615, 1.12808, 1.], fnorms = [3.14183e-05,
3.13437e-05, 3.13039e-05]
Line search: lambdas = [0.91881, 1.08748, 1.25615], fnorms =
[3.12969e-05, 3.13273e-05, 3.14183e-05]
Line search terminated: lambda = 0.918811, fnorms = 3.12969e-05
16 SNES Function norm 3.129688997145e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.09357e-05,
1.58135e-05, 3.12969e-05]
Line search: lambdas = [0.503912, 0.751956, 1.], fnorms =
[1.59287e-05, 2.33645e-05, 3.09357e-05]
Line search: lambdas = [0.0186202, 0.261266, 0.503912], fnorms =
[3.07204e-05, 9.11e-06, 1.59287e-05]
Line search terminated: lambda = 0.342426, fnorms = 1.12885e-05
17 SNES Function norm 1.128846081676e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.09448e-05,
5.94789e-06, 1.12885e-05]
Line search: lambdas = [0.295379, 0.64769, 1.], fnorms =
[8.09996e-06, 4.46782e-06, 3.09448e-05]
Line search: lambdas = [0.48789, 0.391635, 0.295379], fnorms =
[6.07286e-06, 7.07842e-06, 8.09996e-06]
Line search terminated: lambda = 0.997854, fnorms = 3.09222e-05
18 SNES Function norm 3.092215965860e-05
So, in iteration 16, the lambda chosen is 0.91..., even though we see that
lambda close to 0 would give a smaller residual. In iteration 18, we see
that some lambda around 0.65 gives a far smaller residual (approx 4e-6)
than the 0.997... value that gets used (which gives approx 3e-5). The
nonlinear iterations then get caught in some kind of cycle until my
snes_max_it is reached [full log attached].
I guess this is an artifact of (if I understand correctly) trying to
minimize some polynomial fitted to the evaluated values of lambda? But
it's frustrating that it leads to 'obviously wrong' results!
For background information, this comes from an FE discretisation of a
Monge-Ampère equation (and also from several timesteps into a time-varying
problem). For various reasons (related to Monge-Ampère convexity
requirements), I use a partial Jacobian that omits a term from the
linearisation of the residual, and so the nonlinear convergence is not
expected to be quadratic.
Andrew
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0 SNES Function norm 8.029428739596e-02
Line search: lambdas = [1., 0.5, 0.], fnorms = [0.0361527, 0.0477994, 0.0802943]
Line search: lambdas = [0.903514, 0.951757, 1.], fnorms = [0.0352394, 0.0354822, 0.0361527]
Line search: lambdas = [0.90078, 0.902147, 0.903514], fnorms = [0.0352386, 0.0352388, 0.0352394]
Line search terminated: lambda = 0.900703, fnorms = 0.0352386
1 SNES Function norm 3.523861076534e-02
Line search: lambdas = [1., 0.5, 0.], fnorms = [0.0172942, 0.0221407, 0.0352386]
Line search: lambdas = [0.920512, 0.960256, 1.], fnorms = [0.0170826, 0.0171367, 0.0172942]
Line search: lambdas = [0.919798, 0.920155, 0.920512], fnorms = [0.0170826, 0.0170826, 0.0170826]
Line search terminated: lambda = 0.919789, fnorms = 0.0170826
2 SNES Function norm 1.708258531649e-02
Line search: lambdas = [1., 0.5, 0.], fnorms = [0.0110799, 0.0116967, 0.0170826]
Line search: lambdas = [0.799832, 0.899916, 1.], fnorms = [0.0105751, 0.0107035, 0.0110799]
Line search: lambdas = [0.799823, 0.799828, 0.799832], fnorms = [0.0105751, 0.0105751, 0.0105751]
Line search terminated: lambda = 0.805802, fnorms = 0.0105755
3 SNES Function norm 1.057551738195e-02
Line search: lambdas = [1., 0.5, 0.], fnorms = [0.00621427, 0.00740836, 0.0105755]
Line search: lambdas = [0.949881, 0.97494, 1.], fnorms = [0.00619869, 0.0062024, 0.00621427]
Line search: lambdas = [0.951028, 0.950454, 0.949881], fnorms = [0.00619868, 0.00619868, 0.00619869]
Line search terminated: lambda = 0.951032, fnorms = 0.00619868
4 SNES Function norm 6.198678560523e-03
Line search: lambdas = [1., 0.5, 0.], fnorms = [0.00325715, 0.00397891, 0.00619868]
Line search: lambdas = [0.900345, 0.950172, 1.], fnorms = [0.00320353, 0.00321705, 0.00325715]
Line search: lambdas = [0.90023, 0.900287, 0.900345], fnorms = [0.00320353, 0.00320353, 0.00320353]
Line search terminated: lambda = 0.900225, fnorms = 0.00320353
5 SNES Function norm 3.203532630368e-03
Line search: lambdas = [1., 0.5, 0.], fnorms = [0.00198382, 0.00218876, 0.00320353]
Line search: lambdas = [0.842609, 0.921305, 1.], fnorms = [0.00192443, 0.00193951, 0.00198382]
Line search: lambdas = [0.842274, 0.842442, 0.842609], fnorms = [0.00192443, 0.00192443, 0.00192443]
Line search terminated: lambda = 0.842222, fnorms = 0.00192443
6 SNES Function norm 1.924427560912e-03
Line search: lambdas = [1., 0.5, 0.], fnorms = [0.00108014, 0.00131485, 0.00192443]
Line search: lambdas = [0.948983, 0.974492, 1.], fnorms = [0.0010767, 0.00107757, 0.00108014]
Line search: lambdas = [0.948765, 0.948874, 0.948983], fnorms = [0.0010767, 0.0010767, 0.0010767]
Line search terminated: lambda = 0.948764, fnorms = 0.0010767
7 SNES Function norm 1.076698506228e-03
Line search: lambdas = [1., 0.5, 0.], fnorms = [0.000571456, 0.0006976, 0.0010767]
Line search: lambdas = [0.906164, 0.953082, 1.], fnorms = [0.000563516, 0.000565509, 0.000571456]
Line search: lambdas = [0.906231, 0.906197, 0.906164], fnorms = [0.000563516, 0.000563516, 0.000563516]
Line search terminated: lambda = 0.906232, fnorms = 0.000563516
8 SNES Function norm 5.635162562152e-04
Line search: lambdas = [1., 0.5, 0.], fnorms = [0.000339155, 0.000383369, 0.000563516]
Line search: lambdas = [0.865217, 0.932609, 1.], fnorms = [0.000330868, 0.000333796, 0.000339155]
Line search: lambdas = [0.819914, 0.842566, 0.865217], fnorms = [0.000331865, 0.000331154, 0.000330868]
Line search terminated: lambda = 0.869063, fnorms = 0.000330862
9 SNES Function norm 3.308616258520e-04
Line search: lambdas = [1., 0.5, 0.], fnorms = [0.00018679, 0.000227276, 0.000330862]
Line search: lambdas = [0.954185, 0.977092, 1.], fnorms = [0.000186236, 0.000186395, 0.00018679]
Line search: lambdas = [0.950139, 0.952162, 0.954185], fnorms = [0.000186232, 0.000186233, 0.000186236]
Line search terminated: lambda = 0.950139, fnorms = 0.000186232
10 SNES Function norm 1.862321617129e-04
Line search: lambdas = [1., 0.5, 0.], fnorms = [0.000102589, 0.000121554, 0.000186232]
Line search: lambdas = [0.885752, 0.942876, 1.], fnorms = [9.73359e-05, 9.71282e-05, 0.000102589]
Line search: lambdas = [0.916354, 0.901053, 0.885752], fnorms = [9.71018e-05, 9.71834e-05, 9.73359e-05]
Line search terminated: lambda = 0.926324, fnorms = 9.70867e-05
11 SNES Function norm 9.708668367121e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [6.38156e-05, 7.19222e-05, 9.70867e-05]
Line search: lambdas = [0.924516, 0.962258, 1.], fnorms = [6.31271e-05, 6.33834e-05, 6.38156e-05]
Line search: lambdas = [0.889094, 0.906805, 0.924516], fnorms = [6.30484e-05, 6.30681e-05, 6.31271e-05]
Line search terminated: lambda = 0.889093, fnorms = 6.30484e-05
12 SNES Function norm 6.304843182372e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.09866e-05, 4.05459e-05, 6.30484e-05]
Line search: lambdas = [0.957547, 0.978774, 1.], fnorms = [3.08906e-05, 3.09146e-05, 3.09866e-05]
Line search: lambdas = [0.957541, 0.957544, 0.957547], fnorms = [3.08906e-05, 3.08906e-05, 3.08906e-05]
Line search terminated: lambda = 0.95754, fnorms = 3.08906e-05
13 SNES Function norm 3.089060391537e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [1.67849e-05, 2.06894e-05, 3.08906e-05]
Line search: lambdas = [0.942596, 0.971298, 1.], fnorms = [1.67101e-05, 1.67288e-05, 1.67849e-05]
Line search: lambdas = [0.942596, 0.942596, 0.942596], fnorms = [1.67101e-05, 1.67101e-05, 1.67101e-05]
Line search terminated: lambda = 0.942596, fnorms = 1.67101e-05
14 SNES Function norm 1.671012156294e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.22057e-05, 1.12535e-05, 1.67101e-05]
Line search: lambdas = [0.321761, 0.660881, 1.], fnorms = [1.29696e-05, 1.00659e-05, 3.22057e-05]
Line search: lambdas = [0.513941, 0.417851, 0.321761], fnorms = [1.11351e-05, 1.20015e-05, 1.29696e-05]
Line search terminated: lambda = 0.932312, fnorms = 9.21123e-06
15 SNES Function norm 9.211230243067e-06
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.13039e-05, 3.14838e-05, 9.21123e-06]
Line search: lambdas = [1.25615, 1.12808, 1.], fnorms = [3.14183e-05, 3.13437e-05, 3.13039e-05]
Line search: lambdas = [0.91881, 1.08748, 1.25615], fnorms = [3.12969e-05, 3.13273e-05, 3.14183e-05]
Line search terminated: lambda = 0.918811, fnorms = 3.12969e-05
16 SNES Function norm 3.129688997145e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.09357e-05, 1.58135e-05, 3.12969e-05]
Line search: lambdas = [0.503912, 0.751956, 1.], fnorms = [1.59287e-05, 2.33645e-05, 3.09357e-05]
Line search: lambdas = [0.0186202, 0.261266, 0.503912], fnorms = [3.07204e-05, 9.11e-06, 1.59287e-05]
Line search terminated: lambda = 0.342426, fnorms = 1.12885e-05
17 SNES Function norm 1.128846081676e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.09448e-05, 5.94789e-06, 1.12885e-05]
Line search: lambdas = [0.295379, 0.64769, 1.], fnorms = [8.09996e-06, 4.46782e-06, 3.09448e-05]
Line search: lambdas = [0.48789, 0.391635, 0.295379], fnorms = [6.07286e-06, 7.07842e-06, 8.09996e-06]
Line search terminated: lambda = 0.997854, fnorms = 3.09222e-05
18 SNES Function norm 3.092215965860e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08118e-05, 1.54926e-05, 3.09222e-05]
Line search: lambdas = [0.501195, 0.750598, 1.], fnorms = [1.55291e-05, 2.31575e-05, 3.08118e-05]
Line search: lambdas = [0.00203641, 0.251616, 0.501195], fnorms = [3.08595e-05, 7.99583e-06, 1.55291e-05]
Line search terminated: lambda = 0.334898, fnorms = 1.0485e-05
19 SNES Function norm 1.048497478694e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08828e-05, 5.31221e-06, 1.0485e-05]
Line search: lambdas = [0.290564, 0.645282, 1.], fnorms = [7.47161e-06, 3.83131e-06, 3.08828e-05]
Line search: lambdas = [0.482813, 0.386688, 0.290564], fnorms = [5.48866e-06, 6.47839e-06, 7.47161e-06]
Line search terminated: lambda = 1.00084, fnorms = 3.08914e-05
20 SNES Function norm 3.089143204407e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07985e-05, 1.5419e-05, 3.08914e-05]
Line search: lambdas = [0.501004, 0.750502, 1.], fnorms = [1.54499e-05, 2.31217e-05, 3.07985e-05]
Line search: lambdas = [0.000160764, 0.250582, 0.501004], fnorms = [3.08865e-05, 7.76926e-06, 1.54499e-05]
Line search terminated: lambda = 0.334131, fnorms = 1.03269e-05
21 SNES Function norm 1.032685941104e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08661e-05, 5.19297e-06, 1.03269e-05]
Line search: lambdas = [0.289623, 0.644812, 1.], fnorms = [7.35129e-06, 3.71148e-06, 3.08661e-05]
Line search: lambdas = [0.481823, 0.385723, 0.289623], fnorms = [5.37926e-06, 6.36485e-06, 7.35129e-06]
Line search terminated: lambda = 1.00269, fnorms = 3.08938e-05
22 SNES Function norm 3.089378442296e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.0798e-05, 1.54031e-05, 3.08938e-05]
Line search: lambdas = [0.501034, 0.750517, 1.], fnorms = [1.5435e-05, 2.31159e-05, 3.0798e-05]
Line search: lambdas = [6.42473e-05, 0.250549, 0.501034], fnorms = [3.08918e-05, 7.72781e-06, 1.5435e-05]
Line search terminated: lambda = 0.334122, fnorms = 1.02981e-05
23 SNES Function norm 1.029807313639e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08623e-05, 5.16944e-06, 1.02981e-05]
Line search: lambdas = [0.289463, 0.644731, 1.], fnorms = [7.32849e-06, 3.68639e-06, 3.08623e-05]
Line search: lambdas = [0.481655, 0.385559, 0.289463], fnorms = [5.35752e-06, 6.34289e-06, 7.32849e-06]
Line search terminated: lambda = 1.00314, fnorms = 3.08945e-05
24 SNES Function norm 3.089454671948e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.0797e-05, 1.53995e-05, 3.08945e-05]
Line search: lambdas = [0.501053, 0.750526, 1.], fnorms = [1.54319e-05, 2.31143e-05, 3.0797e-05]
Line search: lambdas = [1.90629e-05, 0.250536, 0.501053], fnorms = [3.0894e-05, 7.71862e-06, 1.54319e-05]
Line search terminated: lambda = 0.33412, fnorms = 1.02918e-05
25 SNES Function norm 1.029182711068e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08627e-05, 5.16398e-06, 1.02918e-05]
Line search: lambdas = [0.289427, 0.644713, 1.], fnorms = [7.32328e-06, 3.68069e-06, 3.08627e-05]
Line search: lambdas = [0.481616, 0.385521, 0.289427], fnorms = [5.35247e-06, 6.33781e-06, 7.32328e-06]
Line search terminated: lambda = 1.00305, fnorms = 3.08939e-05
26 SNES Function norm 3.089393766930e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07962e-05, 1.5399e-05, 3.08939e-05]
Line search: lambdas = [0.501055, 0.750528, 1.], fnorms = [1.54315e-05, 2.31138e-05, 3.07962e-05]
Line search: lambdas = [0.750528, 0.625791, 0.501055], fnorms = [2.31138e-05, 1.92726e-05, 1.54315e-05]
Line search terminated: lambda = 0.625791, fnorms = 1.92726e-05
27 SNES Function norm 1.927257601023e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08333e-05, 9.6684e-06, 1.92726e-05]
Line search: lambdas = [0.372429, 0.686214, 1.], fnorms = [1.21184e-05, 6.0938e-06, 3.08333e-05]
Line search: lambdas = [0.562967, 0.467698, 0.372429], fnorms = [8.45935e-06, 1.02887e-05, 1.21184e-05]
Line search terminated: lambda = 1.0029, fnorms = 3.0889e-05
28 SNES Function norm 3.088902536578e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07953e-05, 1.54009e-05, 3.0889e-05]
Line search: lambdas = [0.501012, 0.750506, 1.], fnorms = [1.5432e-05, 2.31134e-05, 3.07953e-05]
Line search: lambdas = [0.750506, 0.625759, 0.501012], fnorms = [2.31134e-05, 1.92726e-05, 1.5432e-05]
Line search terminated: lambda = 0.625759, fnorms = 1.92726e-05
29 SNES Function norm 1.927259393778e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08335e-05, 9.6688e-06, 1.92726e-05]
Line search: lambdas = [0.372426, 0.686213, 1.], fnorms = [1.21188e-05, 6.09436e-06, 3.08335e-05]
Line search: lambdas = [0.562966, 0.467696, 0.372426], fnorms = [8.45983e-06, 1.02891e-05, 1.21188e-05]
Line search terminated: lambda = 1.00294, fnorms = 3.08901e-05
30 SNES Function norm 3.089006071232e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07954e-05, 1.54004e-05, 3.08901e-05]
Line search: lambdas = [0.501022, 0.750511, 1.], fnorms = [1.54319e-05, 2.31134e-05, 3.07954e-05]
Line search: lambdas = [0.750511, 0.625767, 0.501022], fnorms = [2.31134e-05, 1.92725e-05, 1.54319e-05]
Line search terminated: lambda = 0.625767, fnorms = 1.92725e-05
31 SNES Function norm 1.927249100532e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08334e-05, 9.66877e-06, 1.92725e-05]
Line search: lambdas = [0.372425, 0.686213, 1.], fnorms = [1.21187e-05, 6.09434e-06, 3.08334e-05]
Line search: lambdas = [0.562965, 0.467695, 0.372425], fnorms = [8.45981e-06, 1.02891e-05, 1.21187e-05]
Line search terminated: lambda = 1.00295, fnorms = 3.08901e-05
32 SNES Function norm 3.089009716315e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07954e-05, 1.54004e-05, 3.08901e-05]
Line search: lambdas = [0.501022, 0.750511, 1.], fnorms = [1.54318e-05, 2.31134e-05, 3.07954e-05]
Line search: lambdas = [0.750511, 0.625767, 0.501022], fnorms = [2.31134e-05, 1.92725e-05, 1.54318e-05]
Line search terminated: lambda = 0.625767, fnorms = 1.92725e-05
33 SNES Function norm 1.927248677224e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08334e-05, 9.66875e-06, 1.92725e-05]
Line search: lambdas = [0.372425, 0.686213, 1.], fnorms = [1.21187e-05, 6.09431e-06, 3.08334e-05]
Line search: lambdas = [0.562965, 0.467695, 0.372425], fnorms = [8.45979e-06, 1.02891e-05, 1.21187e-05]
Line search terminated: lambda = 1.00295, fnorms = 3.08901e-05
34 SNES Function norm 3.089008816676e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07954e-05, 1.54004e-05, 3.08901e-05]
Line search: lambdas = [0.501023, 0.750511, 1.], fnorms = [1.54318e-05, 2.31133e-05, 3.07954e-05]
Line search: lambdas = [0.750511, 0.625767, 0.501023], fnorms = [2.31133e-05, 1.92725e-05, 1.54318e-05]
Line search terminated: lambda = 0.625767, fnorms = 1.92725e-05
35 SNES Function norm 1.927248252619e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08334e-05, 9.66874e-06, 1.92725e-05]
Line search: lambdas = [0.372425, 0.686212, 1.], fnorms = [1.21187e-05, 6.09431e-06, 3.08334e-05]
Line search: lambdas = [0.562965, 0.467695, 0.372425], fnorms = [8.45979e-06, 1.02891e-05, 1.21187e-05]
Line search terminated: lambda = 1.00295, fnorms = 3.08901e-05
36 SNES Function norm 3.089008385404e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07954e-05, 1.54004e-05, 3.08901e-05]
Line search: lambdas = [0.501023, 0.750511, 1.], fnorms = [1.54318e-05, 2.31133e-05, 3.07954e-05]
Line search: lambdas = [0.750511, 0.625767, 0.501023], fnorms = [2.31133e-05, 1.92725e-05, 1.54318e-05]
Line search terminated: lambda = 0.625767, fnorms = 1.92725e-05
37 SNES Function norm 1.927248268661e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08334e-05, 9.66874e-06, 1.92725e-05]
Line search: lambdas = [0.372425, 0.686212, 1.], fnorms = [1.21187e-05, 6.09431e-06, 3.08334e-05]
Line search: lambdas = [0.562965, 0.467695, 0.372425], fnorms = [8.45979e-06, 1.02891e-05, 1.21187e-05]
Line search terminated: lambda = 1.00295, fnorms = 3.08901e-05
38 SNES Function norm 3.089008466314e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07954e-05, 1.54004e-05, 3.08901e-05]
Line search: lambdas = [0.501023, 0.750511, 1.], fnorms = [1.54318e-05, 2.31133e-05, 3.07954e-05]
Line search: lambdas = [0.750511, 0.625767, 0.501023], fnorms = [2.31133e-05, 1.92725e-05, 1.54318e-05]
Line search terminated: lambda = 0.625767, fnorms = 1.92725e-05
39 SNES Function norm 1.927248319694e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08334e-05, 9.66874e-06, 1.92725e-05]
Line search: lambdas = [0.372425, 0.686212, 1.], fnorms = [1.21187e-05, 6.09431e-06, 3.08334e-05]
Line search: lambdas = [0.562965, 0.467695, 0.372425], fnorms = [8.45979e-06, 1.02891e-05, 1.21187e-05]
Line search terminated: lambda = 1.00295, fnorms = 3.08901e-05
40 SNES Function norm 3.089008515441e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07954e-05, 1.54004e-05, 3.08901e-05]
Line search: lambdas = [0.501023, 0.750511, 1.], fnorms = [1.54318e-05, 2.31133e-05, 3.07954e-05]
Line search: lambdas = [0.750511, 0.625767, 0.501023], fnorms = [2.31133e-05, 1.92725e-05, 1.54318e-05]
Line search terminated: lambda = 0.625767, fnorms = 1.92725e-05
41 SNES Function norm 1.927248317403e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08334e-05, 9.66874e-06, 1.92725e-05]
Line search: lambdas = [0.372425, 0.686212, 1.], fnorms = [1.21187e-05, 6.09431e-06, 3.08334e-05]
Line search: lambdas = [0.562965, 0.467695, 0.372425], fnorms = [8.45979e-06, 1.02891e-05, 1.21187e-05]
Line search terminated: lambda = 1.00295, fnorms = 3.08901e-05
42 SNES Function norm 3.089008504499e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07954e-05, 1.54004e-05, 3.08901e-05]
Line search: lambdas = [0.501023, 0.750511, 1.], fnorms = [1.54318e-05, 2.31133e-05, 3.07954e-05]
Line search: lambdas = [0.750511, 0.625767, 0.501023], fnorms = [2.31133e-05, 1.92725e-05, 1.54318e-05]
Line search terminated: lambda = 0.625767, fnorms = 1.92725e-05
43 SNES Function norm 1.927248310466e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08334e-05, 9.66874e-06, 1.92725e-05]
Line search: lambdas = [0.372425, 0.686212, 1.], fnorms = [1.21187e-05, 6.09431e-06, 3.08334e-05]
Line search: lambdas = [0.562965, 0.467695, 0.372425], fnorms = [8.45979e-06, 1.02891e-05, 1.21187e-05]
Line search terminated: lambda = 1.00295, fnorms = 3.08901e-05
44 SNES Function norm 3.089008496293e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07954e-05, 1.54004e-05, 3.08901e-05]
Line search: lambdas = [0.501023, 0.750511, 1.], fnorms = [1.54318e-05, 2.31133e-05, 3.07954e-05]
Line search: lambdas = [0.750511, 0.625767, 0.501023], fnorms = [2.31133e-05, 1.92725e-05, 1.54318e-05]
Line search terminated: lambda = 0.625767, fnorms = 1.92725e-05
45 SNES Function norm 1.927248310274e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08334e-05, 9.66874e-06, 1.92725e-05]
Line search: lambdas = [0.372425, 0.686212, 1.], fnorms = [1.21187e-05, 6.09431e-06, 3.08334e-05]
Line search: lambdas = [0.562965, 0.467695, 0.372425], fnorms = [8.45979e-06, 1.02891e-05, 1.21187e-05]
Line search terminated: lambda = 1.00295, fnorms = 3.08901e-05
46 SNES Function norm 3.089008495811e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07954e-05, 1.54004e-05, 3.08901e-05]
Line search: lambdas = [0.501023, 0.750511, 1.], fnorms = [1.54318e-05, 2.31133e-05, 3.07954e-05]
Line search: lambdas = [0.750511, 0.625767, 0.501023], fnorms = [2.31133e-05, 1.92725e-05, 1.54318e-05]
Line search terminated: lambda = 0.625767, fnorms = 1.92725e-05
47 SNES Function norm 1.927248310735e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08334e-05, 9.66874e-06, 1.92725e-05]
Line search: lambdas = [0.372425, 0.686212, 1.], fnorms = [1.21187e-05, 6.09431e-06, 3.08334e-05]
Line search: lambdas = [0.562965, 0.467695, 0.372425], fnorms = [8.45979e-06, 1.02891e-05, 1.21187e-05]
Line search terminated: lambda = 1.00295, fnorms = 3.08901e-05
48 SNES Function norm 3.089008495408e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.07954e-05, 1.54004e-05, 3.08901e-05]
Line search: lambdas = [0.501023, 0.750511, 1.], fnorms = [1.54318e-05, 2.31133e-05, 3.07954e-05]
Line search: lambdas = [0.750511, 0.625767, 0.501023], fnorms = [2.31133e-05, 1.92725e-05, 1.54318e-05]
Line search terminated: lambda = 0.625767, fnorms = 1.92725e-05
49 SNES Function norm 1.927248310443e-05
Line search: lambdas = [1., 0.5, 0.], fnorms = [3.08334e-05, 9.66874e-06, 1.92725e-05]
Line search: lambdas = [0.372425, 0.686212, 1.], fnorms = [1.21187e-05, 6.09431e-06, 3.08334e-05]
Line search: lambdas = [0.562965, 0.467695, 0.372425], fnorms = [8.45979e-06, 1.02891e-05, 1.21187e-05]
Line search terminated: lambda = 1.00295, fnorms = 3.08901e-05
50 SNES Function norm 3.089008494267e-05
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