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In many real-world optimization problems, the objective function may come from a simulation evaluation so that it is (a) subject to various levels of noise, (b) not differentiable, and (c) computationally hard to evaluate. In this paper, we modify Powell's UOBYQA algorithm to handle those real-world simulation problems. Our modifications apply Bayesian(More)
DIRECT (DIviding RECTangles) is a deterministic global optimization algorithm for bound-constrained problems. The algorithm, based on a space-partitioning scheme, performs both global exploration and local exploitation. In this paper, we modify the deterministic DIRECT algorithm to handle noisy function optimization. We adopt a simple approach that(More)
We describe the application of a Bayesian variable-number sample-path (VNSP) optimization algorithm to yield a robust design for a floating sleeve antenna for hepatic microwave ablation. Finite element models are used to generate the electromagnetic (EM) field and thermal distribution in liver given a particular design. Dielectric properties of the tissue(More)
We investigate an on-line planning strategy for the fractionated radio-therapy planning problem, which incorporates the effects of day-today patient motion. On-line planning demonstrates significant improvement over off-line strategies in terms of reducing registration error, but it requires extra work in the replanning procedures, such as in the CT scans(More)
We investigate the use of optimization and data mining techniques for calibrating the input parameters to a discrete event simulation code. In the context of a breast-cancer epidemiology model we show how a hierarchical classifier can accurately predict those parameters that ensure the simulation replicates benchmark data within 95% confidence intervals. We(More)
We propose a robust portfolio optimization approach based on Value-at-Risk (VaR) adjusted Sharpe ratios. Traditional Sharpe ratio estimates using a limited series of historical returns are subject to estimation errors. Portfolio optimization based on traditional Sharpe ratios ignores this uncertainty and, as a result, is not robust. In this paper, we(More)
Principal Protected Absolute Return Barrier Notes (ARBNs) are structured products linked to an underlying security or an index. While these notes guarantee principal protection – return of face value – their upside potential is dependent on the level of the underlying security never falling outside of a predefined range. This, combined with the credit risk(More)
Since first introduced in 2003, the number of autocallable structured products in the U.S. has increased exponentially. The autocall feature immediately converts the product if the reference asset's value rises above a pre-specified call price. Because an autocallable structured product matures immediately if it is called, the autocall feature reduces the(More)