1Blurb:: 2Interval analysis using local optimization 3Description:: 4Interval analysis using local methods (\c local_interval_est). 5If the problem is amenable to local optimization 6methods (e.g. can provide derivatives or use finite difference 7method to calculate derivatives), then one can use one of two local 8methods to calculate these bounds. 9\li \c sqp 10\li \c nip 11 12<b> Additional Resources </b> 13 14Refer to \ref topic-variable_support for information on supported 15variable types. 16 17Topics:: uncertainty_quantification, epistemic_uncertainty_quantification_methods, interval_estimation 18Examples:: 19Theory:: 20In interval analysis, one assumes that nothing is known about 21an epistemic uncertain variable except that its value lies 22somewhere within an interval. In this situation, it is NOT 23assumed that the value has a uniform probability of occuring 24within the interval. Instead, the interpretation is that 25any value within the interval is a possible value or a potential 26realization of that variable. In interval analysis, the 27uncertainty quantification problem is one of determining the 28resulting bounds on the output (defining the output interval) 29given interval bounds on the inputs. Again, any output response 30that falls within the output interval is a possible output 31with no frequency information assigned to it. 32 33Faq:: 34See_Also:: method-global_evidence, method-global_interval_est, method-local_evidence 35