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Update installation docs

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Change-Id: I14cc0d6f0ad4bfeee3d60a42d9b57bc5deaad01b
This commit is contained in:
Sameer Agarwal
2022-03-03 14:44:01 -08:00
parent 1a377d7078
commit a65e738851
3 changed files with 82 additions and 143 deletions
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docs/source/gradient_tutorial.rst
+5 -4
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@@ -110,7 +110,7 @@ algorithm.
33: f: 4.835994e-17 d: 1.00e-12 g: 1.05e-07 h: 1.13e-06 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 6.59e-04
34: f: 1.885250e-22 d: 4.84e-17 g: 2.69e-10 h: 1.45e-08 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 6.67e-04
Solver Summary (v 2.0.0-eigen-(3.3.9)-lapack-suitesparse-(5.8.1)-cxsparse-(3.2.0)-acceleratesparse-eigensparse-no_openmp-no_custom_blas)
Solver Summary (v 2.1.0-eigen-(3.4.0)-lapack-suitesparse-(5.10.1)-cxsparse-(3.2.0)-acceleratesparse-eigensparse-no_openmp)
Parameters 2
Line search direction LBFGS (20)
@@ -127,9 +127,9 @@ algorithm.
Time (in seconds):
Cost evaluation 0.000000 (0)
Gradient & cost evaluation 0.000008 (44)
Polynomial minimization 0.000067
Total 0.000721
Gradient & cost evaluation 0.000005 (44)
Polynomial minimization 0.000041
Total 0.000368
Termination: CONVERGENCE (Parameter tolerance reached. Relative step_norm: 1.890726e-11 <= 1.000000e-08.)
@@ -137,6 +137,7 @@ algorithm.
Final x: 1 y: 1
If you are unable to use automatic differentiation for some reason
(say beacause you need to call an external library), then you can
use numeric differentiation. In that case the functor is defined as