PerformanceEstimation / PEPit

PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.
https://pepit.readthedocs.io/en/latest/
MIT License
79 stars 12 forks source link

Adding 2 new operator classes. #88

Closed bgoujaud closed 1 year ago

bgoujaud commented 1 year ago

It adds the operator classes "cocoercive strongly monotone" and "negatively comonotone".

It also fixes some minor typos here and there.

About the negative comonotonicity. It has been added for readability but is exactly the same as strong monotonicity with a negative parameter. Is it really useful?

codecov-commenter commented 1 year ago

Codecov Report

Attention: 5 lines in your changes are missing coverage. Please review.

Comparison is base (6b1a6c3) 89.72% compared to head (5fb1eb5) 90.04%.

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Additional details and impacted files ```diff @@ Coverage Diff @@ ## master #88 +/- ## ========================================== + Coverage 89.72% 90.04% +0.31% ========================================== Files 151 154 +3 Lines 5012 5111 +99 ========================================== + Hits 4497 4602 +105 + Misses 515 509 -6 ``` | [Files](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation) | Coverage Δ | | |---|---|---| | [...onstrained\_convex\_minimization/gradient\_descent.py](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation#diff-UEVQaXQvZXhhbXBsZXMvdW5jb25zdHJhaW5lZF9jb252ZXhfbWluaW1pemF0aW9uL2dyYWRpZW50X2Rlc2NlbnQucHk=) | `79.16% <100.00%> (ø)` | | | [PEPit/functions/\_\_init\_\_.py](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation#diff-UEVQaXQvZnVuY3Rpb25zL19faW5pdF9fLnB5) | `100.00% <100.00%> (ø)` | | | [PEPit/functions/smooth\_convex\_function.py](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation#diff-UEVQaXQvZnVuY3Rpb25zL3Ntb290aF9jb252ZXhfZnVuY3Rpb24ucHk=) | `85.71% <ø> (ø)` | | | [PEPit/operators/\_\_init\_\_.py](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation#diff-UEVQaXQvb3BlcmF0b3JzL19faW5pdF9fLnB5) | `100.00% <100.00%> (ø)` | | | [PEPit/operators/cocoercive.py](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation#diff-UEVQaXQvb3BlcmF0b3JzL2NvY29lcmNpdmUucHk=) | `93.33% <100.00%> (ø)` | | | [PEPit/operators/lipschitz.py](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation#diff-UEVQaXQvb3BlcmF0b3JzL2xpcHNjaGl0ei5weQ==) | `93.33% <100.00%> (ø)` | | | [PEPit/operators/lipschitz\_strongly\_monotone.py](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation#diff-UEVQaXQvb3BlcmF0b3JzL2xpcHNjaGl0el9zdHJvbmdseV9tb25vdG9uZS5weQ==) | `94.11% <100.00%> (ø)` | | | [PEPit/operators/monotone.py](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation#diff-UEVQaXQvb3BlcmF0b3JzL21vbm90b25lLnB5) | `100.00% <100.00%> (ø)` | | | [PEPit/operators/strongly\_monotone.py](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation#diff-UEVQaXQvb3BlcmF0b3JzL3N0cm9uZ2x5X21vbm90b25lLnB5) | `100.00% <100.00%> (ø)` | | | [PEPit/primitive\_steps/epsilon\_subgradient\_step.py](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation#diff-UEVQaXQvcHJpbWl0aXZlX3N0ZXBzL2Vwc2lsb25fc3ViZ3JhZGllbnRfc3RlcC5weQ==) | `100.00% <ø> (ø)` | | | ... and [9 more](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88?src=pr&el=tree-more&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation) | | ... and [1 file with indirect coverage changes](https://app.codecov.io/gh/PerformanceEstimation/PEPit/pull/88/indirect-changes?src=pr&el=tree-more&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=PerformanceEstimation)

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