Dakota Reference Manual: local_interval_est

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local_interval_est

Interval analysis using local optimization

## Topics

This keyword is related to the topics:

- uncertainty_quantification
- epistemic_uncertainty_quantification_methods
- interval_estimation

## Specification

Alias: nond_local_interval_est

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional (Choose One) | Optimization Solver (Group 1) | 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.

## See Also

These keywords may also be of interest:

- global_evidence
- global_interval_est
- local_evidence