Add proximal ADMM solvers #371
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@@ Coverage Diff @@
## main #371 +/- ##
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- Coverage 94.15% 94.08% -0.07%
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Files 57 61 +4
Lines 3406 3824 +418
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+ Hits 3207 3598 +391
- Misses 199 226 +27
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scico/examples/scripts/denoise_tv_multi.py Lines 60 to 65 in 8a1ea91 This is probably caused by the operators being jitted the first time they are called. |
| snp.vstack( | ||
| (hist_admm.Objective, hist_ladmm.Objective, hist_padmm.Objective, hist_pdhg.Objective) | ||
| ).T, | ||
| snp.vstack((hist_admm.Time, hist_ladmm.Time, hist_padmm.Time, hist_pdhg.Time)).T, | ||
| ptyp="semilogy", | ||
| title="Objective function", | ||
| xlbl="Time (s)", |
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Some of the curves still appear to a large horizontal shift, as if there is a lot of compute happening before iteration 1. Suggestion: add a comment on this if you have a reason why.
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This is not occurring when the script is run locally without a GPU. Will investigate.
| 2. Proximal ADMM has similar performance to PDHG with respect to iterations, | ||
| but is slightly inferior with respect to time. | ||
| 3. ADMM greatly outperforms Linearized ADMM with respect to iterations. | ||
| 4. ADMM slightly outperforms Linearized ADMM with respect to time. This is | ||
| possible because the ADMM $\mathbf{x}$-update can be solved relatively | ||
| cheaply, with only 2 CG iterations. If more CG iterations were required, | ||
| the time comparison would be favorable to Linearized ADMM. |
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not clear, if more CG iterations were required which one would be superior to which other one?
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I'm not sure what's not clear. Does "favorable" seem ambiguous?
Add proximal ADMM solvers.