Discrete least-norm approximation by nonnegative (trigonometric) polynomials and rational functions

Polynomials, trigonometric polynomials, and rational functions are widely used for the discrete approximation of functions or simulation models. Often, it is known beforehand that the underlying unknown function has certain properties, e.g., nonnegative or increasing on a certain region. However, the approximation may not inherit these properties automatically. We present some methodology (using semidefinite programming and results from real algebraic geometry) for least-norm approximation by polynomials, trigonometric polynomials, and rational functions that preserve nonnegativity.

[1]  D. Hilbert Über die Darstellung definiter Formen als Summe von Formenquadraten , 1888 .

[2]  J. Kalbfleisch Statistical Inference Under Order Restrictions , 1975 .

[3]  G. Watson Approximation theory and numerical methods , 1980 .

[4]  M. Powell,et al.  Approximation theory and methods , 1984 .

[5]  F. T. Wright,et al.  Order restricted statistical inference , 1988 .

[6]  Douglas M. Bates,et al.  Nonlinear Regression Analysis and Its Applications , 1988 .

[7]  Olga Taussky-Todd SOME CONCRETE ASPECTS OF HILBERT'S 17TH PROBLEM , 1996 .

[8]  Heike Faßbender On numerical methods for discrete least-squares approximation by trigonometric polynomials , 1997, Math. Comput..

[9]  F. Kuijt Convexity preserving interpolation - stationary nonlinear subdivision and splines , 1998 .

[10]  Jos F. Sturm,et al.  A Matlab toolbox for optimization over symmetric cones , 1999 .

[11]  Yurii Nesterov,et al.  Squared Functional Systems and Optimization Problems , 2000 .

[12]  Xiong Zhang,et al.  Solving Large-Scale Sparse Semidefinite Programs for Combinatorial Optimization , 1999, SIAM J. Optim..

[13]  B. Reznick,et al.  Polynomials that are positive on an interval , 2000 .

[14]  J. Brian Gray,et al.  Introduction to Linear Regression Analysis , 2002, Technometrics.

[15]  Tomas Jansson,et al.  Using surrogate models and response surfaces in structural optimization – with application to crashworthiness design and sheet metal forming , 2003 .

[16]  P. Parrilo,et al.  From coefficients to samples: a new approach to SOS optimization , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[17]  Roummel F. Marcia,et al.  Convex Quadratic Approximation , 2004, Comput. Optim. Appl..

[18]  Markus Schweighofer,et al.  Optimization of Polynomials on Compact Semialgebraic Sets , 2005, SIAM J. Optim..

[19]  Won-Sun Ruy,et al.  Polynomial genetic programming for response surface modeling Part 1: a methodology , 2005 .

[20]  L. Nilsson,et al.  On polynomial response surfaces and Kriging for use in structural optimization of crashworthiness , 2005 .

[21]  R.B. Lenin,et al.  Adaptive multivariate rational data fitting with applications in electromagnetics , 2006, IEEE Transactions on Microwave Theory and Techniques.

[22]  Etienne de Klerk,et al.  Global optimization of rational functions: a semidefinite programming approach , 2006, Math. Program..