loadTitle=Select Algorithm
algorithm=Algorithm
dataTabText=Data
dataTabToolTipText=Data
chartTabText=Diagram
chartTabToolTipText=Diagram
configurationTreeDimensionsLabel=Dimensions
configurationTreeMeasuresLabel=Measures
copyToClipboardLabel=Copy selection to clipboard
propertyLabel=Property
removeTraceActionLabel=remove
addTraceButtonLabel=Add trace
saveData=Save data
targetFunction=Objective functions (Inputs)
constraints=Constraints (Inputs)
designVariables=Design variables (Outputs)
algorithmProperties=Algorithm properties...
excelExport=Export table to Excel file
newGradientText=Note: This will add %d new input(s) for gradients.
pythonForMethodInstalled=Note: Algorithm requires Python version 2.6 or 2.7 to be installed.
noDataErrorTitle=Data error
noDataError=One or both axes not defined for graph
startValueInput=Init (Inputs)
optimalSolutionOutput=Optimal design variables (Outputs)
dakotaOSHint=RCE includes Dakota version 6.0, tested on Windows 10, Windows Server 2019, Ubuntu 18.04, and CentOS 8.\nOther distributions can be used if Dakota is self-compiled (see Optimizer help for that).
restoreDefaultAlgorithmProperties=Restore defaults
noteDiscreteMethods=Note: Some Dakota algorithms do not support discrete optimization. In case only discrete design variables are used, these algorithms are not available. \nNote that if continuous and discrete variables are used simultaneously, the discrete design variables might be ignored during optimization.