Results 221 to 230 of about 701,348 (263)

Approximate shape fitting via linearization

Proceedings 42nd IEEE Symposium on Foundations of Computer Science, 2001
Shape fitting is a fundamental optimization problem in computer science. The authors present a general and unified technique for solving a certain family of such problems. Given a point set P in R/sup d/, this technique can be used to /spl epsi/-approximate: (i) the min-width annulus and shell that contains P, (ii) minimum width cylindrical shell ...
Sariel Har-Peled, Kasturi R. Varadarajan
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The use of linear programming in curve fitting

Computers & Chemistry, 1981
Abstract The mechanical technique if linear programming is employed to resolve multiple simultaneous exponetials and multiple unresolved Gaussians into thier simple components. The necessary theory is derived for the resolution of the linear combinations of any family of non-linear terms.
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Automatic curve-fitting-II. Linear equations

Computers & Chemistry, 1978
Abstract In this second paper of a three-part series, the automatic FORTRAN IV curve-fitting program, CURFIT, that can handle both linear and non-linear equations is described and demonstrated with linear equations. In CURFIT the maximum tolerance (or uncertainty) for each item is an integral part of the data.
Paul A. D. de Maine, G. K. Springer, R. A. Mikelskas   +2 more
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An Application of Linear Programming to Curve Fitting

Journal of the Society for Industrial and Applied Mathematics, 1958
Property (a) makes the problem meaningful, since the norm is bounded below by zero. Interestingly, properties (b) and (c) imply that the norm is a convex function of h, a fact easily shown. Thus, the theory of approximations of the type illustrated above reduces to a theory of finding minima of convex functions.
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A Method for Fitting Linear Combinations of Exponentials

Biometrics, 1962
Many situationis in biology, epidemiology, economics and the physical sciences yield data which can best be described by linear combinations of exponelntials. This is particularly true in biological radiation, growth and tracer studies. In this paper it will be shown that the method of partial totals may be used to fit linear combinations of any number
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Non-linear curve fitting

2022
This chapter focuses on non-linear curve fitting. Since real empirical data can be complicated, using a model can help reduce the complexity by summarizing it using one or more parameter estimates. The chapter looks at the issue of parameter optimization and the whole class of computational techniques designed to solve it.
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Fitting Data Sequences to Linear Systems

1997
In this introduction, we review some basic notions and results from the behavioral approach to dynamical systems. We refer to [7, 10, 11] for a more tho`rough exposition of this theory.
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Using linear and non-linear regression to fit biochemical data

Trends in Biochemical Sciences, 1990
For biochemists or chemists the most common form of data analysis is likely to be regression analysis. This is a technique to find the 'best' values for various experimental parameters; defined as those values which, when used in an appropriate equation, result in the minimum deviation of the calculated results from the experimental data.
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Fitting data with linear regression

2004
Abstract Linear regression can be viewed as just a special case of nonlinear regression. Any nonlinear regression program can be used to fit to a linear model, and the results will be the same as if you had chosen linear regression. Because linear regression is usually performed separately from nonlinear regression, we explain linear ...
Harvey Motulsky, Arthur Christopoulos
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The Linearity of First-Fit Coloring of Interval Graphs

SIAM Journal on Discrete Mathematics, 1988
It is shown that first-fit coloring requires at most 40\(\omega\) colors to color an interval graph with clique size \(\omega\). It follows that a polynomial time approximation algorithm for dynamic storage allocation due to Chrobak and Slusarek has a constant performance ratio of 80.
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