Results 1  10
of
1,731,215
approximation errors
, 2010
"... For purposes of actual evaluation, mathematical functions f are commonly replaced by approximation polynomials p. Examples include floatingpoint implementations of elementary functions, quadrature or more theoretical proof work involving transcendental functions. Replacing f by p induces a relative ..."
Abstract
 Add to MetaCart
For purposes of actual evaluation, mathematical functions f are commonly replaced by approximation polynomials p. Examples include floatingpoint implementations of elementary functions, quadrature or more theoretical proof work involving transcendental functions. Replacing f by p induces a
Approximation Errors
, 2008
"... A bound on the error introduced by truncating a quantum addition is given. This bound shows that only a few controlled rotation gates will be necessary to get a reliable computation. ..."
Abstract
 Add to MetaCart
A bound on the error introduced by truncating a quantum addition is given. This bound shows that only a few controlled rotation gates will be necessary to get a reliable computation.
Approximation Error Maps
, 2001
"... Let F and A be two linear function spaces defined on some domain \Omega, and let * be a vector seminorm for the space A + F. We consider here the question of how well A approximates F in the sense of the metric *. Global error measures are insuficiently informative when the space A is not spati ..."
Abstract
 Add to MetaCart
Let F and A be two linear function spaces defined on some domain \Omega, and let * be a vector seminorm for the space A + F. We consider here the question of how well A approximates F in the sense of the metric *. Global error measures are insuficiently informative when the space A
Exact and Approximated Error of the FMA
, 2011
"... The fused multiply accumulateadd (FMA) instruction, specified by the IEEE 7542008 Standard for FloatingPoint Arithmetic, eases some calculations, and is already available on some current processors such as the Power PC or the Itanium. We first extend an earlier work on the computation of the exa ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
of the exact error of an FMA (by giving more general conditions and providing a formal proof). Then, we present a new algorithm that computes an approximation to the error of an FMA, and provide error bounds and a formal proof for that algorithm.
COMPARISON OF THE APPROXIMATION ERRORS OF THE PREFILTERED
"... Abstract—We study the performance of the linear prediction (LP) method for blind channel identification when the true channel is of order, whereas the channel model is of order, with.By partitioning the true channel into the thorder significant part and the unmodeled tails, we show that the LP meth ..."
Abstract
 Add to MetaCart
the approximation performance of a special FIR prefilter. The results show that the convergent rate of the prefiltered projection is the same as that of the orthogonal projection. In addition, for bandlimited signals, the quantitative estimates of the upper bounds of the three types of errors are obtained
Sample Complexity versus Approximation Error
"... We consider the problem of learning an unknown realvalued function from a sequence of values of the function at randomly chosen points when the estimates are constrained to some class of functions called the hypothesis class. Ideally, thehypothesis class should be able to approximate a wide variety ..."
Abstract
 Add to MetaCart
variety of target functions, yet not need an excessive number of examples to ensure that a learning algorithm can choose a nearoptimal approximation to the target function. We show that these two objectives are incompatible, in the sense that as the approximation error of the hypothesis class decreases
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
Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics
 J. Geophys. Res
, 1994
"... . A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The ..."
Abstract

Cited by 782 (22 self)
 Add to MetaCart
. A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter
Surface Simplification Using Quadric Error Metrics
"... Many applications in computer graphics require complex, highly detailed models. However, the level of detail actually necessary may vary considerably. To control processing time, it is often desirable to use approximations in place of excessively detailed models. We have developed a surface simplifi ..."
Abstract

Cited by 1178 (14 self)
 Add to MetaCart
simplification algorithm which can rapidly produce high quality approximations of polygonal models. The algorithm uses iterative contractions of vertex pairs to simplify models and maintains surface error approximations using quadric matrices. By contracting arbitrary vertex pairs (not just edges), our algorithm
Results 1  10
of
1,731,215