Interval analysis using local optimization


uncertainty_quantification, epistemic_uncertainty_quantification_methods, interval_estimation


  • Alias: nond_local_interval_est

  • Arguments: None

Child Keywords:


Description of Group

Dakota Keyword

Dakota Keyword Description

Optional (Choose One)

Optimization Solver


Use a sequential quadratic programming method for solving an optimization sub-problem


Use a nonlinear interior point method for solving an optimization sub-problem



Stopping criterion based on objective function or statistics convergence



Identifier for model block to be used by a method


Interval analysis using local methods ( local_interval_est). If the problem is amenable to local optimization methods (e.g. can provide derivatives or use finite difference method to calculate derivatives), then one can use one of two local methods to calculate these bounds.

  • sqp

  • nip

Additional Resources

Refer to Variable Support for information on supported variable types.


In interval analysis, one assumes that nothing is known about an epistemic uncertain variable except that its value lies somewhere within an interval. In this situation, it is NOT assumed that the value has a uniform probability of occuring within the interval. Instead, the interpretation is that any value within the interval is a possible value or a potential realization of that variable. In interval analysis, the uncertainty quantification problem is one of determining the resulting bounds on the output (defining the output interval) given interval bounds on the inputs. Again, any output response that falls within the output interval is a possible output with no frequency information assigned to it.