parameters: (self, problem ,deltaJ, deltaX, maxIt)
return_statement: return optimizer.optimize()
docstring: Run the given optimization problem problem
docstring_summary: Run the given optimization problem problem
docstring_tokens: Run the given optimization problem problem
function: def __runOptimization(self, problem ,deltaJ, deltaX, maxIt):
          """Run the given optimization problem problem"""
  
          print "run new optimisation with initial values:"
          self.__printResults()
  
          # verbose and choldmod solving with schur complement trick
          options = aopt.Optimizer2Options()
          options.verbose = True
          options.linearSolver = aopt.BlockCholeskyLinearSystemSolver()
          options.doSchurComplement = True
  
          # stopping criteria
          options.maxIterations = maxIt
          options.convergenceDeltaJ = deltaJ
          options.convergenceDeltaX = deltaX
  
          # use the dogleg trustregion policy
          options.trustRegionPolicy = aopt.DogLegTrustRegionPolicy()
  
          # create the optimizer
          optimizer = aopt.Optimizer2(options)
          optimizer.setProblem(problem)
  
          # go for it:
          return optimizer.optimize()
function_tokens: def __runOptimization ( self , problem , deltaJ , deltaX , maxIt ) : print "run new optimisation with initial values:" self . __printResults ( ) # verbose and choldmod solving with schur complement trick options = aopt . Optimizer2Options ( ) options . verbose = True options . linearSolver = aopt . BlockCholeskyLinearSystemSolver ( ) options . doSchurComplement = True # stopping criteria options . maxIterations = maxIt options . convergenceDeltaJ = deltaJ options . convergenceDeltaX = deltaX # use the dogleg trustregion policy options . trustRegionPolicy = aopt . DogLegTrustRegionPolicy ( ) # create the optimizer optimizer = aopt . Optimizer2 ( options ) optimizer . setProblem ( problem ) # go for it: return optimizer . optimize ( )