Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Approximate IPA: Trading Unbiasedness for Simplicity   When Perturbation Analysis (PA) yields unbiased sensitivity estimators for
expected-value performance functions in discrete event dynamic systems, it can
be used for performance optimization of those functions. However, when PA is
known to be unbiased, the complexity of its estimators often does not scale
with the system's size. The purpose of this paper is to suggest an alternative
approach to optimization which balances precision with computing efforts by
trading off complicated, unbiased PA estimators for simple, biased approximate
estimators. Furthermore, we provide guidelines for developing such estimators,
that are largely based on the Stochastic Flow Modeling framework. We suggest
that if the relative error (or bias) is not too large, then optimization
algorithms such as stochastic approximation converge to a (local) minimum just
like in the case where no approximation is used. We apply this approach to an
example of balancing loss with buffer-cost in a finite-buffer queue, and prove
a crucial upper bound on the relative error. This paper presents the initial
study of the proposed approach, and we believe that if the idea gains traction
then it may lead to a significant expansion of the scope of PA in optimization
of discrete event systems.

27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
1
The categories of this paper are: cs
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat