4,501 research outputs found
On the rates of convergence of simulation based optimization algorithms for optimal stopping problems
In this paper we study simulation based optimization algorithms for solving
discrete time optimal stopping problems. This type of algorithms became popular
among practioneers working in the area of quantitative finance. Using large
deviation theory for the increments of empirical processes, we derive optimal
convergence rates and show that they can not be improved in general. The rates
derived provide a guide to the choice of the number of simulated paths needed
in optimization step, which is crucial for the good performance of any
simulation based optimization algorithm. Finally, we present a numerical
example of solving optimal stopping problem arising in option pricing that
illustrates our theoretical findings
Quantum-Enhanced Simulation-Based Optimization
In this paper, we introduce a quantum-enhanced algorithm for simulation-based
optimization. Simulation-based optimization seeks to optimize an objective
function that is computationally expensive to evaluate exactly, and thus, is
approximated via simulation. Quantum Amplitude Estimation (QAE) can achieve a
quadratic speed-up over classical Monte Carlo simulation. Hence, in many cases,
it can achieve a speed-up for simulation-based optimization as well. Combining
QAE with ideas from quantum optimization, we show how this can be used not only
for continuous but also for discrete optimization problems. Furthermore, the
algorithm is demonstrated on illustrative problems such as portfolio
optimization with a Value at Risk constraint and inventory management.Comment: 9 pages, 9 figure
Chromoionophores in optical ion sensors
The feasibility of surface plasmon resonance (SPR) for ion sensing has been investigated. Emphasis is laid on a simulation-based optimization of the SP carrying structure, as well as the applicability of a specific chemoâoptical interface we have developed. A preliminary result is presented
A derivative-free approach for a simulation-based optimization problem in healthcare
Hospitals have been challenged in recent years to deliver high quality care with limited resources. Given the pressure to contain costs,developing procedures for optimal resource allocation becomes more and more critical in this context. Indeed, under/overutilization of emergency room and ward resources can either compromise a hospital's ability to provide the best possible care, or result in precious funding going toward underutilized resources. Simulation--based optimization tools then help facilitating the planning and management of hospital services, by maximizing/minimizing some specific indices (e.g. net profit) subject to given clinical and economical constraints.
In this work, we develop a simulation--based optimization approach for the resource planning of a specific hospital ward. At each step, we first consider a suitably chosen resource setting and evaluate both efficiency and satisfaction of the restrictions by means of a discrete--event simulation model. Then, taking into account the information obtained by the simulation process, we use a derivative--free optimization algorithm to modify the given setting. We report results for a real--world problem coming from the obstetrics ward of an Italian hospital showing both the effectiveness and the efficiency of the proposed approach
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