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 |
Uses a sequential quadratic programming method for underlying optimization |
|
Uses a nonlinear interior point method for underlying optimization |
|||
Optional |
Stopping criterion based on objective function or statistics convergence |
||
Optional |
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.