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myCobyla= class=Cobyla class=OptimizationAlgorithmImplementation problem=class=OptimizationProblem implementation=class=OptimizationProblemImplementation objective=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[] evaluationImplementation=class=NoEvaluation name=Unnamed gradientImplementation=class=NoGradient name=Unnamed hessianImplementation=class=NoHessian name=Unnamed equality constraint=none inequality constraint=none bounds=none minimization=true dimension=0 startingPoint=class=Point name=Unnamed dimension=2 values=[5,2.1] maximumIterationNumber=100 maximumCallsNumber=200 maximumAbsoluteError=1e-10 maximumRelativeError=1e-10 maximumResidualError=1e-10 maximumConstraintError=1e-10 rhoBeg=0.1
FORM= class=FORM class=Analytical nearestPointAlgorithm=class=OptimizationAlgorithm implementation=class=Cobyla class=OptimizationAlgorithmImplementation problem=class=OptimizationProblem implementation=class=OptimizationProblemImplementation objective=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[] evaluationImplementation=class=NoEvaluation name=Unnamed gradientImplementation=class=NoGradient name=Unnamed hessianImplementation=class=NoHessian name=Unnamed equality constraint=none inequality constraint=none bounds=none minimization=true dimension=0 startingPoint=class=Point name=Unnamed dimension=2 values=[5,2.1] maximumIterationNumber=100 maximumCallsNumber=200 maximumAbsoluteError=1e-10 maximumRelativeError=1e-10 maximumResidualError=1e-10 maximumConstraintError=1e-10 rhoBeg=0.1 event=class=RandomVector implementation=class=ThresholdEventImplementation antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x0,x1,y0] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x0,x1] outputVariablesNames=[y0] formulas=[-(6+x0^2-x1)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x0,x1] outputVariablesNames=[y0] formulas=[-(6+x0^2-x1)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x0,x1] outputVariablesNames=[y0] formulas=[-(6+x0^2-x1)] antecedent=class=UsualRandomVector distribution=class=Normal name=Normal dimension=2 mean=class=Point name=Unnamed dimension=2 values=[5,2.1] sigma=class=Point name=Unnamed dimension=2 values=[3.3,3] correlationMatrix=class=CorrelationMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[1,0,0,1] operator=class=Greater name=Unnamed threshold=0 physicalstartingPoint=class=Point name=Unnamed dimension=2 values=[5,2.1] result=class=FORMResult class=AnalyticalResult standardSpaceDesignPoint=class=Point name=Unnamed dimension=0 values=[] physicalSpaceDesignPoint=class=Point name=Unnamed dimension=0 values=[] limitStateVariable=class=RandomVector implementation=class=ThresholdEventImplementation antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[] evaluationImplementation=class=NoEvaluation name=Unnamed gradientImplementation=class=NoGradient name=Unnamed hessianImplementation=class=NoHessian name=Unnamed antecedent=class=RandomVectorImplementation operator=class=Less name=Unnamed threshold=0 isStandardPointOriginInFailureSpace=false hasoferReliabilityIndex=0 importanceFactors=class=PointWithDescription name=Unnamed dimension=0 description=[] values=[] classicalImportanceFactors=class=PointWithDescription name=Unnamed dimension=0 description=[] values=[] hasoferReliabilityIndexSensitivity=[] eventProbability=0 generalisedReliabilityIndex=0 eventProbabilitySensitivity=[]
importance factors= [Marginal 1 : 0.502588, Marginal 2 : 0.497412]
Hasofer reliability index=1.941922
result= class=FORMResult class=AnalyticalResult standardSpaceDesignPoint=class=Point name=Standard Space Design Point dimension=2 values=[-1.3767,1.36959] physicalSpaceDesignPoint=class=Point name=Physical Space Design Point dimension=2 values=[0.456905,6.20876] limitStateVariable=class=RandomVector implementation=class=ThresholdEventImplementation antecedent=class=CompositeRandomVector function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x0,x1,y0] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x0,x1] outputVariablesNames=[y0] formulas=[-(6+x0^2-x1)] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x0,x1] outputVariablesNames=[y0] formulas=[-(6+x0^2-x1)] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[x0,x1] outputVariablesNames=[y0] formulas=[-(6+x0^2-x1)] antecedent=class=UsualRandomVector distribution=class=Normal name=Normal dimension=2 mean=class=Point name=Unnamed dimension=2 values=[5,2.1] sigma=class=Point name=Unnamed dimension=2 values=[3.3,3] correlationMatrix=class=CorrelationMatrix dimension=2 implementation=class=MatrixImplementation name=Unnamed rows=2 columns=2 values=[1,0,0,1] operator=class=Greater name=Unnamed threshold=0 isStandardPointOriginInFailureSpace=false hasoferReliabilityIndex=1.94192 importanceFactors=class=PointWithDescription name=Importance Factors dimension=2 description=[Marginal 1,Marginal 2] values=[0.502588,0.497412] classicalImportanceFactors=class=PointWithDescription name=Unnamed dimension=0 description=[] values=[] hasoferReliabilityIndexSensitivity=[] eventProbability=0.0260733 generalisedReliabilityIndex=1.94192 eventProbabilitySensitivity=[]
hasoferReliabilityIndexSensitivity = [class=PointWithDescription name=Marginal 1 dimension=2 description=[mu_0,sigma_0] values=[0.214829,-0.295754],class=PointWithDescription name=Marginal 2 dimension=2 description=[mu_1,sigma_1] values=[-0.235091,-0.321978],class=PointWithDescription name=dependence dimension=1 description=[R_1_0] values=[0]]
eventProbabilitySensitivity = [class=PointWithDescription name=Marginal 1 dimension=2 description=[mu_0,sigma_0] values=[-0.0130055,0.0179046],class=PointWithDescription name=Marginal 2 dimension=2 description=[mu_1,sigma_1] values=[0.0142322,0.0194922],class=PointWithDescription name=dependence dimension=1 description=[R_1_0] values=[-0]]
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