On using perturbation analysis to do sensitivity analysis: derivatives versus differences

Abstract

One use of perturbation analysis is to compute the derivative of a performance measure P with respect to a parameter theta for use in iterative optimization schemes to locate local maxima by driving the derivative to zero. It has been pointed out that in stochastic approximation contexts, there is an advantage in using derivatives as opposed to differences. Another use of perturbation analysis is just to do a sensitivity analysis with respect to a parameter without relation to any optimization. The advantages of using differences rather than derivatives for sensitivity analysis are identified. This prompts the use of noninfinitesimal perturbation analysis (or other techniques) whereby perturbation information can be obtained from a single run.>

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 49 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.