Results 1  10
of
420,244
AN ADAPTIVE MULTIDIMENSIONAL VERSION OF THE KIEFERWOLFOWITZ STOCHASTIC APPROXIMATION ALGORITHM
"... We extend the scaledandshifted KieferWolfowitz (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 pe ..."
Abstract
 Add to MetaCart
the performance of this algorithm to the traditional KieferWolfowitz (KW) one and observe significant improvement in the finitetime performance on some stylized test functions and a multidimensional newsvendor problem. 1
Multivariate Stochastic Approximation Using a Simultaneous Perturbation Gradient Approximation
 IEEE TRANSACTIONS ON AUTOMATIC CONTROL
, 1992
"... Consider the problem of finding a root of the multivariate gradient equation that arises in function minimization. When only noisy measurements of the function are available, a stochastic approximation (SA) algorithm of the general KieferWolfowitz type is appropriate for estimating the root. This p ..."
Abstract

Cited by 312 (14 self)
 Add to MetaCart
Consider the problem of finding a root of the multivariate gradient equation that arises in function minimization. When only noisy measurements of the function are available, a stochastic approximation (SA) algorithm of the general KieferWolfowitz type is appropriate for estimating the root
JOHNSON t MARK ALLYN. On the KieferWolfowitz Process and Some of Its Modifications.
"... process is a stochastic approximation procedure used to estimate the value of a controlled variable that maximizes the expected response (M(x» of a corresponding dependent random variable. An extension of a theorem on the asymptotic distribution of a certain class of stochastic approximation procedu ..."
Abstract
 Add to MetaCart
process is a stochastic approximation procedure used to estimate the value of a controlled variable that maximizes the expected response (M(x» of a corresponding dependent random variable. An extension of a theorem on the asymptotic distribution of a certain class of stochastic approximation
Approximate Signal Processing
, 1997
"... It is increasingly important to structure signal processing algorithms and systems to allow for trading off between the accuracy of results and the utilization of resources in their implementation. In any particular context, there are typically a variety of heuristic approaches to managing these tra ..."
Abstract

Cited by 516 (2 self)
 Add to MetaCart
these tradeoffs. One of the objectives of this paper is to suggest that there is the potential for developing a more formal approach, including utilizing current research in Computer Science on Approximate Processing and one of its central concepts, Incremental Refinement. Toward this end, we first summarize a
Random key predistribution schemes for sensor networks
 IN PROCEEDINGS OF THE 2003 IEEE SYMPOSIUM ON SECURITY AND PRIVACY
, 2003
"... Key establishment in sensor networks is a challenging problem because asymmetric key cryptosystems are unsuitable for use in resource constrained sensor nodes, and also because the nodes could be physically compromised by an adversary. We present three new mechanisms for key establishment using the ..."
Abstract

Cited by 813 (14 self)
 Add to MetaCart
the framework of predistributing a random set of keys to each node. First, in the qcomposite keys scheme, we trade off the unlikeliness of a largescale network attack in order to significantly strengthen random key predistribution’s strength against smallerscale attacks. Second, in the multipath
A Digital Fountain Approach to Reliable Distribution of Bulk Data
 IN PROC. OF ACM SIGCOMM ’98
, 1998
"... The proliferation of applications that must reliably distribute bulk data to a large number of autonomous clients motivates the design of new multicast and broadcast prot.ocols. We describe an ideal, fully scalable protocol for these applications that we call a digital fountain. A digital fountain a ..."
Abstract

Cited by 498 (20 self)
 Add to MetaCart
allows any number of heterogeneous clients to acquire bulk data with optimal efficiency at times of their choosing. Moreover, no feedback channels are needed to ensure reliable delivery, even in the face of high loss rates. We develop a protocol that closely approximates a digital fountain using a new
A KieferWolfowitz comparison theorem for Wicksell’s problem
 Ann. Statist
, 2007
"... We extend the isotonic analysis for the Wicksell’s problem to estimate a regression function, which is motivated by the problem of estimating dark matter distribution in Astronomy. The main result is a version of the KieferWolfowitz theorem comparing the empirical distribution to its least concave ..."
Abstract

Cited by 8 (3 self)
 Add to MetaCart
We extend the isotonic analysis for the Wicksell’s problem to estimate a regression function, which is motivated by the problem of estimating dark matter distribution in Astronomy. The main result is a version of the KieferWolfowitz theorem comparing the empirical distribution to its least concave
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
Abstract

Cited by 1513 (20 self)
 Add to MetaCart
law), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude f  (1) ≥ f  (2) ≥... ≥ f  (N), and define the weakℓp ball
Greedy Function Approximation: A Gradient Boosting Machine
 Annals of Statistics
, 2000
"... Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additi ..."
Abstract

Cited by 951 (12 self)
 Add to MetaCart
Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed
Results 1  10
of
420,244