local_interval_est

Interval analysis using local optimization

Topics

uncertainty_quantification, epistemic_uncertainty_quantification_methods, interval_estimation

Specification

• Alias: nond_local_interval_est

• Arguments: None

Child Keywords:

Required/Optional	Description of Group	Dakota Keyword	Dakota Keyword Description
Optional (Choose One)	Optimization Solver	sqp	Uses a sequential quadratic programming method for underlying optimization
		nip	Uses a nonlinear interior point method for underlying optimization
Optional		convergence_tolerance	Stopping criterion based on objective function or statistics convergence
Optional		model_pointer	Identifier for model block to be used by a method

Description

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.

Theory

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.