D-optimal designs for full and reduced Fourier regression models

2015 ◽  
Vol 58 (3) ◽  
pp. 811-829 ◽  
Author(s):  
Xiaojian Xu ◽  
Xiaoli Shang
Keyword(s):  
Regression Models ◽  
Optimal Designs ◽  
Fourier Regression

D-Optimal Designs for Quadratic Regression Models

2000 ◽  
pp. 189-195 ◽  
Author(s):  
E. E. M. van Berkum ◽  
B. Pauwels ◽  
P. M. Upperman
Keyword(s):  
Regression Models ◽  
Optimal Designs ◽  
Quadratic Regression

scholarly journals On computational complexity of construction of c -optimal linear regression models over finite experimental domains

2012 ◽  
Vol 51 (1) ◽  
pp. 11-21
Author(s):  
Jaromír Antoch ◽  
Michal Černý ◽  
Milan Hladík
Keyword(s):  
Linear Programming ◽  
Regression Models ◽  
Parallel Implementation ◽  
Optimal Designs ◽  
Polynomial Algorithms ◽  
Linear Regression Models ◽  
Optimal Linear ◽  
New Algorithms ◽  
Approximate Version ◽  
Strongly Polynomial

ABSTRACT Recent complexity-theoretic results on finding c-optimal designs over finite experimental domain X are discussed and their implications for the analysis of existing algorithms and for the construction of new algorithms are shown. Assuming some complexity-theoretic conjectures, we show that the approximate version of c-optimality does not have an efficient parallel implementation. Further, we study the question whether for finding the c-optimal designs over finite experimental domain X there exist a strongly polynomial algorithms and show relations between considered design problem and linear programming. Finally, we point out some complexity-theoretic properties of the SAC algorithm for c-optimality.

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