Migrate mixed optimizer to OSS #2573
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This pull request was exported from Phabricator. Differential Revision: D64349675 |
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Summary: Pull Request resolved: pytorch#2573 Differential Revision: D64349675
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #2573 +/- ##
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Coverage 99.98% 99.98%
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Files 195 196 +1
Lines 17122 17333 +211
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+ Hits 17119 17330 +211
Misses 3 3 ☔ View full report in Codecov by Sentry. |
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Summary: Pull Request resolved: pytorch#2573 Moves `optimize_acqf_mixed_alternating` to OSS BoTorch. This is an optimizer designed for mixed (low-cardinality) integer and continuous variables. It alternates between discrete and continuous optimization steps to optimize the acquisition function. The discrete step greedily searches over the integer variables, moving to the 1-Manhattan distance neighbor that offers the greatest improvement at each step. The continuous steps calls to `optimize_acqf` to optimize the continuous variables, while keeping the discrete dimensions fixed. Differential Revision: D64349675
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Summary: Pull Request resolved: pytorch#2573 Moves `optimize_acqf_mixed_alternating` to OSS BoTorch. This is an optimizer designed for mixed (low-cardinality) integer and continuous variables. It alternates between discrete and continuous optimization steps to optimize the acquisition function. The discrete step greedily searches over the integer variables, moving to the 1-Manhattan distance neighbor that offers the greatest improvement at each step. The continuous steps calls to `optimize_acqf` to optimize the continuous variables, while keeping the discrete dimensions fixed. Differential Revision: D64349675
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…oad the correct commit data. Differential Revision: D64348157
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Summary: Pull Request resolved: pytorch#2573 Moves `optimize_acqf_mixed_alternating` to OSS BoTorch. This is an optimizer designed for mixed (low-cardinality) integer and continuous variables. It alternates between discrete and continuous optimization steps to optimize the acquisition function. The discrete step greedily searches over the integer variables, moving to the 1-Manhattan distance neighbor that offers the greatest improvement at each step. The continuous steps calls to `optimize_acqf` to optimize the continuous variables, while keeping the discrete dimensions fixed. Reviewed By: Balandat Differential Revision: D64349675
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This pull request was exported from Phabricator. Differential Revision: D64349675 |
Summary: Pull Request resolved: pytorch#2573 Moves `optimize_acqf_mixed_alternating` to OSS BoTorch. This is an optimizer designed for mixed (low-cardinality) integer and continuous variables. It alternates between discrete and continuous optimization steps to optimize the acquisition function. The discrete step greedily searches over the integer variables, moving to the 1-Manhattan distance neighbor that offers the greatest improvement at each step. The continuous steps calls to `optimize_acqf` to optimize the continuous variables, while keeping the discrete dimensions fixed. Reviewed By: Balandat Differential Revision: D64349675
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Differential Revision: D64349675