Two classes of multiplicative algorithms for constructing optimizing distributions

DOI10.1016/j.csda.2006.05.014zbMath1157.62330OpenAlexW2072367497MaRDI QIDQ1010510

J. Martínez

Publication date: 6 April 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2006.05.014

zbMATH Keywords

optimality conditionsoptimal designsmultiplicative algorithmsoptimal distributions on spacesvertex directional derivatives

Mathematics Subject Classification ID

Related Items (9)

Optimal Design of Experiments for Implicit Models ⋮ Efficient computational algorithm for optimal allocation in regression models ⋮ Multiplicative methods for computing \(D\)-optimal stratified designs of experiments ⋮ Adaptive grid semidefinite programming for finding optimal designs ⋮ Strict monotonicity and convergence rate of Titterington's algorithm for computing D-optimal designs ⋮ A new monotonic algorithm for the \(E\)-optimal experiment design problem ⋮ Barycentric algorithm for computing D-optimal size- and cost-constrained designs of experiments ⋮ Monotonic convergence of a general algorithm for computing optimal designs ⋮ Multiplicative algorithms for computing optimum designs

Uses Software

• MODA

Cites Work

• Constructing optimal designs with constraints
• Convergent design sequences, for sufficiently regular optimality criteria, II: Singular case
• General equivalence theory for optimum designs (approximate theory)
• Construction of optimal designs using a clustering approach
• An algorithm for optimal designs on a design space
• Some iterative procedures for generating nonsingular optimal designs
• Algorithms for the construction of optimizing distributions
• Construction of Constrained Optimal Designs
• The Sequential Generation of $D$-Optimum Experimental Designs
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Two classes of multiplicative algorithms for constructing optimizing distributions