Deniz M. Cicek

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We consider the Kiefer-Wolfowitz (KW) stochastic approximation algorithm and derive general upper bounds on its meansquared error. The bounds are established using an elementary induction argument and phrased directly in the terms of tuning sequences of the algorithm. From this we deduce the nonnecessity of one of the main assumptions imposed on the tuning(More)
We extend the scaled-and-shifted Kiefer-Wolfowitz (SSKW) algorithm developed by Broadie, Cicek, and Zeevi (2009) to multiple dimensions. The salient feature of this algorithm is that it makes adjustments of the tuning parameters that adapt to the underlying problem characteristics. We compare the performance of this algorithm to the traditional(More)
We consider prototypical sequential stochastic optimization methods of Robbins-Monro (RM), Kiefer-Wolfowitz (KW), and Simultaneous Perturbations Stochastic Approximation (SPSA) varieties and propose adaptive modifications for multidimensional applications. These adaptive versions dynamically scale and shift the tuning sequences to better match the(More)
A design for a unique artificial-hair-cell-type sensor (AHCTS) based entirely on 3D-structured, vertically aligned carbon nanotube (CNT) bundles is introduced. Standard microfabrication techniques were used for the straightforward micro-nano integration of vertically aligned carbon nanotube arrays composed of low-layer multi-walled CNTs (two to six layers).(More)
Following the great depressions methodology suggested by Kehoe and Prescott (2002, 2007), we use growth accounting and perfect foresight dynamic general equilibrium models to study growth performance of Turkey from 1968 to 2004. Our benchmark model without any frictions and taxes accounts for 86% of the observed change in the growth rate of GDP perworking(More)
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