-
cc.mallet.optimize.LimitedMemoryBFGS.optimize()
-
cc.mallet.optimize.Optimizer.optimize()
-
com.dotcms.content.elasticsearch.business.ContentletIndexAPI.optimize()
-
com.dotcms.repackage.org.elasticsearch.client.IndicesAdminClient.optimize()
-
com.esri.gpt.server.assertion.index.AsnIndexAdapter.optimize()
-
com.facebook.presto.sql.planner.ExpressionInterpreter.optimize()
-
com.facebook.presto.sql.relational.optimizer.ExpressionOptimizer.optimize()
-
com.google.caja.ancillary.opt.JsOptimizer.optimize()
Returns an optimized version of the concatenation of the programs registered via {@link #addInput}.
-
com.google.javascript.jscomp.type.FlowScope.optimize()
Optimize this scope and return a new FlowScope with faster lookup.
-
com.googlecode.pngtastic.core.PngOptimizer.optimize()
-
com.redhat.ceylon.compiler.Options.optimize()
-
com.sk89q.worldedit.internal.expression.Expression.optimize()
-
com.volantis.mcs.dom2theme.impl.optimizer.DefaultStyledDOMOptimizer.optimize()
-
com.volantis.mcs.dom2theme.impl.optimizer.HeterogeneousShorthandOptimizer.optimize()
-
com.volantis.mcs.dom2theme.impl.optimizer.ShorthandOptimizer.optimize()
-
com.volantis.mcs.dom2theme.impl.optimizer.StyledDOMOptimizer.optimize()
Analyze the document optimizing the properties.
@param styledDom The document to optimize.
@return The list of the resulting properties, one entry for eachelement in the input document.
-
com.volantis.mcs.dom2theme.impl.optimizer.border.BorderOptimizer.optimize()
-
de.mpi.rgblab.optimizer.Optimizer.optimize()
-
edu.brown.optimizer.PlanOptimizer.optimize()
Main entry point for the PlanOptimizer
-
edu.brown.optimizer.optimizations.AbstractOptimization.optimize()
Perform the optimization on the given PlanNode tree Returns a pair containing the new root of the tree a boolean flag that signals whether the tree was modified or not
@param rootNode
@return
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edu.cmu.sphinx.result.LatticeOptimizer.optimize()
Code for optimizing Lattices. An optimal lattice has all the same paths as the original, but with fewer nodes and edges
Note that these methods are all in Lattice so that it is easy to change the definition of "equivalent" nodes and edges. For example, an equivalent node might have the same word, but start or end at a different time.
To experiment with other definitions of equivalent, just create a superclass of Lattice.
-
net.sf.saxon.expr.ExpressionVisitor.optimize()
Optimize an expression, via the ExpressionVisitor
@param exp the expression to be typechecked
@param contextItemType the static type of the context item for this expression
@return the rewritten expression
@throws XPathException
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net.sourceforge.jFuzzyLogic.optimization.OptimizationDeltaJump.optimize()
Gradient optimization for error functions Gradient descent algorithm It also does a line search to get a good 'next point' on each iteration
@param verbose : Be verbose (recomended, because it takes some time to converge)
-
org.antlr.analysis.DFAOptimizer.optimize()
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org.apache.blur.thrift.generated.Blur.Iface.optimize()
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org.apache.commons.math.optimization.MultivariateRealOptimizer.optimize()
Optimizes an objective function.
@param f objective function
@param goalType type of optimization goal: either {@link GoalType#MAXIMIZE}or {@link GoalType#MINIMIZE}
@param startPoint the start point for optimization
@return the point/value pair giving the optimal value for objective function
@exception FunctionEvaluationException if the objective function throws one duringthe search
@exception OptimizationException if the algorithm failed to converge
@exception IllegalArgumentException if the start point dimension is wrong
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org.apache.commons.math.optimization.UnivariateRealOptimizer.optimize()
Find an optimum in the given interval.
An optimizer may require that the interval brackets a single optimum.
@param f the function to optimize.
@param goalType type of optimization goal: either {@link GoalType#MAXIMIZE}or {@link GoalType#MINIMIZE}.
@param min the lower bound for the interval.
@param max the upper bound for the interval.
@return a value where the function is optimum.
@throws ConvergenceException if the maximum iteration count is exceededor the optimizer detects convergence problems otherwise.
@throws FunctionEvaluationException if an error occurs evaluating the function.
@throws IllegalArgumentException if min > max or the endpoints do notsatisfy the requirements specified by the optimizer.
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org.apache.commons.math.optimization.direct.MultiDirectional.optimize()
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org.apache.commons.math.optimization.direct.NelderMead.optimize()
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org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer.optimize()
-
org.apache.commons.math.optimization.linear.SimplexSolver.optimize()
-
org.apache.commons.math.optimization.univariate.BrentOptimizer.optimize()
{@inheritDoc}
-
org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer.optimize()
{@inheritDoc}
@param optData Optimization data. In addition to those documented in{@link MultivariateOptimizer#parseOptimizationData(OptimizationData[]) MultivariateOptimizer}, this method will register the following data:
@return {@inheritDoc}
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org.apache.commons.math3.optimization.MultivariateOptimizer.optimize()
{@inheritDoc}
@param optData Optimization data. The following data will be looked for:
- {@link org.apache.commons.math3.optim.MaxEval}
- {@link org.apache.commons.math3.optim.InitialGuess}
- {@link org.apache.commons.math3.optim.SimpleBounds}
- {@link org.apache.commons.math3.optim.nonlinear.vector.Target}
- {@link org.apache.commons.math3.optim.nonlinear.vector.Weight}
- {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunction}
- {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunctionJacobian}
@return {@inheritDoc}
@throws TooManyEvaluationsException if the maximal number ofevaluations is exceeded.
@throws DimensionMismatchException if the initial guess, target, and weightarguments have inconsistent dimensions.
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org.apache.commons.math3.optimization.general.GaussNewtonOptimizer.optimize()
{@inheritDoc}
@param optData Optimization data. The following data will be looked for:
- {@link org.apache.commons.math3.optim.MaxEval}
- {@link org.apache.commons.math3.optim.InitialGuess}
- {@link org.apache.commons.math3.optim.SimpleBounds}
- {@link org.apache.commons.math3.optim.nonlinear.vector.Target}
- {@link org.apache.commons.math3.optim.nonlinear.vector.Weight}
- {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunction}
- {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunctionJacobian}
@return {@inheritDoc}
@throws TooManyEvaluationsException if the maximal number ofevaluations is exceeded.
@throws DimensionMismatchException if the initial guess, target, and weightarguments have inconsistent dimensions.
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org.apache.commons.math3.optimization.general.NonLinearConjugateGradientOptimizer.optimize()
{@inheritDoc}
@param optData Optimization data. The following data will be looked for:
- {@link org.apache.commons.math3.optim.MaxEval}
- {@link org.apache.commons.math3.optim.InitialGuess}
- {@link org.apache.commons.math3.optim.SimpleBounds}
- {@link org.apache.commons.math3.optim.nonlinear.vector.Target}
- {@link org.apache.commons.math3.optim.nonlinear.vector.Weight}
- {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunction}
- {@link org.apache.commons.math3.optim.nonlinear.vector.ModelFunctionJacobian}
@return {@inheritDoc}
@throws TooManyEvaluationsException if the maximal number ofevaluations is exceeded.
@throws DimensionMismatchException if the initial guess, target, and weightarguments have inconsistent dimensions.
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org.apache.derby.impl.sql.compile.QueryTreeNode.optimize()
Generates an optimized QueryTree from a bound QueryTree. Actually, it annotates the tree in place rather than generating a new tree, but this interface allows the root node of the optmized QueryTree to be different from the root node of the bound QueryTree. For non-optimizable statements (for example, CREATE TABLE), return the bound tree without doing anything. For optimizable statements, this method will be over-ridden in the statement's root node (DMLStatementNode in all cases we know about so far). Throws an exception if the tree is not bound, or if the binding is out of date.
@return An optimized QueryTree
@exception StandardException Thrown on error
-
org.apache.hadoop.hive.ql.optimizer.Optimizer.optimize()
invoke all the transformations one-by-one, and alter the query plan
@return ParseContext
@throws SemanticException
-
org.apache.hadoop.hive.ql.optimizer.physical.PhysicalOptimizer.optimize()
invoke all the resolvers one-by-one, and alter the physical plan.
@return PhysicalContext
@throws HiveException
-
org.apache.lucene.index.IndexModifier.optimize()
Merges all segments together into a single segment, optimizing an index for search.
@see IndexWriter#optimize()
@throws IllegalStateException if the index is closed
-
org.apache.lucene.index.IndexWriter.optimize()
This method has been deprecated, as it is horribly inefficient and very rarely justified. Lucene's multi-segment search performance has improved over time, and the default TieredMergePolicy now targets segments with deletions.
@deprecated
-
org.apache.lucene.index.RandomIndexWriter.optimize()
Forces an optimize.
NOTE: this should be avoided in tests unless absolutely necessary, as it will result in less test coverage.
@see IndexWriter#optimize()
-
org.apache.maven.index.context.IndexingContext.optimize()
Optimizes index. According to Lucene 3.6+ Javadoc, there is no more sense to optimize, so this method might become "noop".
-
org.apache.pig.experimental.logical.optimizer.LogicalPlanOptimizer.optimize()
-
org.apache.pig.experimental.plan.optimizer.PlanOptimizer.optimize()
Run the optimizer. This method attempts to match each of the Rules against the plan. If a Rule matches, it then calls the check method of the associated Transformer to give the it a chance to check whether it really wants to do the optimization. If that returns true as well, then Transformer.transform is called.
@throws OptimizerException
-
org.apache.pig.impl.logicalLayer.optimizer.LogicalOptimizer.optimize()
-
org.apache.pig.impl.logicalLayer.optimizer.Optimizer.optimize()
Optimize the given {@link LogicalPlan} if feasible and return status.
@param root root of the {@link LogicalPlan} to optimize
@return true
if optimization was feasible and was effected,false
otherwise.
-
org.apache.pig.impl.logicalLayer.optimizer.streaming.LoadOptimizer.optimize()
-
org.apache.pig.impl.logicalLayer.optimizer.streaming.StoreOptimizer.optimize()
-
org.apache.pig.newplan.logical.optimizer.LogicalPlanOptimizer.optimize()
-
org.apache.pig.newplan.logical.relational.LogicalPlan.optimize()
-
org.apache.pig.newplan.optimizer.PlanOptimizer.optimize()
Run the optimizer. This method attempts to match each of the Rules against the plan. If a Rule matches, it then calls the check method of the associated Transformer to give the it a chance to check whether it really wants to do the optimization. If that returns true as well, then Transformer.transform is called.
@throws FrontendException
-
org.apache.pig.pen.util.FunctionalLogicalOptimizer.optimize()
-
org.apache.pig.test.TestExperimentalFilterAboveForeach.MyPlanOptimizer.optimize()
-
org.apache.pig.test.TestLogicalOptimizer.LogicalOptimizerDerivative.optimize()
-
org.apache.solr.client.solrj.SolrServer.optimize()
Performs an explicit optimize, causing a merge of all segments to one.
waitFlush=true and waitSearcher=true to be inline with the defaults for plain HTTP access
Note: In most cases it is not required to do explicit optimize
@throws SolrServerException
@throws IOException
-
org.apache.solr.client.solrj.impl.CommonsHttpSolrServer.optimize()
-
org.apache.solr.client.solrj.impl.HttpSolrServer.optimize()
-
org.apache.tajo.engine.planner.LogicalOptimizer.optimize()
-
org.elasticsearch.index.shard.service.IndexShard.optimize()
-
org.exist.indexing.lucene.LuceneIndexWorker.optimize()
Optimize the Lucene index by merging all segments into a single one. This may take a while and write operations will be blocked during the optimize.
-
org.exist.indexing.range.RangeIndexWorker.optimize()
Optimize the Lucene index by merging all segments into a single one. This may take a while and write operations will be blocked during the optimize.
@see org.apache.lucene.index.IndexWriter#forceMerge(int)
-
org.gd.spark.opendl.util.MyConjugateGradient.optimize()
-
org.h2.expression.Expression.optimize()
Try to optimize the expression.
@param session the session
@return the optimized expression
-
org.hibernate.search.SearchFactory.optimize()
Optimize all indexes
-
org.hibernate.search.store.optimization.OptimizerStrategy.optimize()
Allows the implementation to start an optimization process. The decision of optimizing or not is up to the implementor. This is invoked after all changes of a transaction are applied, but never during stream operation such as those used by the MassIndexer.
@param workspace the current work space
-
org.jnode.vm.compiler.ir.IRControlFlowGraph.optimize()
-
org.odftoolkit.odfdom.incubator.doc.office.OdfOfficeAutomaticStyles.optimize()
This methods removes all automatic styles that are currently not used by any styleable element. Additionally all duplicate automatic styles will be removed.
-
org.openrdf.query.algebra.evaluation.util.QueryOptimizerList.optimize()
-
org.pdf4j.saxon.expr.ExpressionVisitor.optimize()
Optimize an expression, via the ExpressionVisitor
@param exp the expression to be typechecked
@param contextItemType the static type of the context item for this expression
@return the rewritten expression
@throws XPathException
-
org.renjin.compiler.pipeline.optimize.Optimizers.optimize()
-
org.rssowl.core.persist.service.IModelSearch.optimize()
Optimizes the search index.
@throws PersistenceException
-
org.sonatype.nexus.index.context.IndexingContext.optimize()
-
org.springmodules.lucene.index.core.DefaultLuceneIndexTemplate.optimize()
-
org.springmodules.lucene.index.factory.LuceneIndexWriter.optimize()
@see IndexWriter#optimize()
@see IOException
-
org.teiid.query.optimizer.BatchedUpdatePlanner.optimize()
Optimizes batched updates by batching all contiguous commands that relate to the same physical model. For example, for the following batch of commands:
- 1. INSERT INTO physicalModel.myPhysical ...
- 2. UPDATE physicalModel.myPhysical ...
- 3. DELETE FROM virtualmodel.myVirtual ...
- 4. UPDATE virtualmodel.myVirtual ...
- 5. UPDATE physicalModel.myOtherPhysical ...
- 6. INSERT INTO physicalModel.myOtherPhysical ...
-
- 7. DELETE FROM physicalModel.myOtherPhysical ...
- 8. INSERT INTO physicalModel.myPhysical ...
- 9. INSERT INTO physicalModel.myPhysical ...
- 10. INSERT INTO physicalModel.myPhysical ...
- 11. INSERT INTO physicalModel.myPhysical ...
- 12. INSERT INTO physicalModel.myPhysical ...
this implementation will batch as follows: (1,2), (5, 6, 7), (8 thru 12). The remaining commands/plans will be executed individually.
@see org.teiid.query.optimizer.CommandPlanner#optimize(Command,org.teiid.core.id.IDGenerator,org.teiid.query.metadata.QueryMetadataInterface,org.teiid.query.optimizer.capabilities.CapabilitiesFinder,org.teiid.query.analysis.AnalysisRecord,CommandContext)
@since 4.2
-
org.teiid.query.optimizer.relational.RelationalPlanner.optimize()
-
org.toubassi.femtozip.dictionary.DictionaryOptimizer.optimize()
-
tv.floe.metronome.deeplearning.neuralnetwork.optimize.VectorizedNonZeroStoppingConjugateGradient.optimize()
-
tv.floe.metronome.deeplearning.neuralnetwork.optimize.util.CustomConjugateGradient.optimize()
-
uk.ac.ucl.panda.indexing.io.IndexWriter.optimize()
samer-threads.com/lists/lucene/java-dev/47895 for more discussion.
Note that this can require substantial temporary free space in the Directory (see LUCENE-764 for details):
-
If no readers/searchers are open against the index, then free space required is up to 1X the total size of the starting index. For example, if the starting index is 10 GB, then you must have up to 10 GB of free space before calling optimize.
-
If readers/searchers are using the index, then free space required is up to 2X the size of the starting index. This is because in addition to the 1X used by optimize, the original 1X of the starting index is still consuming space in the Directory as the readers are holding the segments files open. Even on Unix, where it will appear as if the files are gone ("ls" won't list them), they still consume storage due to "delete on last close" semantics.
Furthermore, if some but not all readers re-open while the optimize is underway, this will cause > 2X temporary space to be consumed as those new readers will then hold open the partially optimized segments at that time. It is best not to re-open readers while optimize is running.
The actual temporary usage could be much less than these figures (it depends on many factors).
In general, once the optimize completes, the total size of the index will be less than the size of the starting index. It could be quite a bit smaller (if there were many pending deletes) or just slightly smaller.
If an Exception is hit during optimize(), for example due to disk full, the index will not be corrupt and no documents will have been lost. However, it may have been partially optimized (some segments were merged but not all), and it's possible that one of the segments in the index will be in non-compound format even when using compound file format. This will occur when the Exception is hit during conversion of the segment into compound format.
This call will optimize those segments present in the index when the call started. If other threads are still adding documents and flushing segments, those newly created segments will not be optimized unless you call optimize again.
@throws CorruptIndexException if the index is corrupt
@throws IOException if there is a low-level IO error