TY - GEN A1 - Sagnol, Guillaume A1 - Harman, Radoslav T1 - Computing exact D-optimal designs by mixed integer second order cone programming N2 - Let the design of an experiment be represented by an $s$-dimensional vector $\vec{w}$ of weights with non-negative components. Let the quality of $\vec{w}$ for the estimation of the parameters of the statistical model be measured by the criterion of $D$-optimality defined as the $m$-th root of the determinant of the information matrix $M(\vec{w})=\sum_{i=1}^s w_iA_iA_i^T$, where $A_i$, $i=1,...,s$, are known matrices with $m$ rows. In the paper, we show that the criterion of $D$-optimality is second-order cone representable. As a result, the method of second order cone programming can be used to compute an approximate $D$-optimal design with any system of linear constraints on the vector of weights. More importantly, the proposed characterization allows us to compute an \emph{exact} $D$-optimal design, which is possible thanks to high-quality branch-and-cut solvers specialized to solve mixed integer second order cone problems. We prove that some other widely used criteria are also second order cone representable, for instance the criteria of $A$-, and $G$-optimality, as well as the criteria of $D_K$- and $A_K$-optimality, which are extensions of $D$-, and $A$-optimality used in the case when only a specific system of linear combinations of parameters is of interest. We present several numerical examples demonstrating the efficiency and universality of the proposed method. We show that in many cases the mixed integer second order cone programming approach allows us to find a provably optimal exact design, while the standard heuristics systematically miss the optimum. T3 - ZIB-Report - 13-39 KW - Optimal Design of Experiments KW - D-optimality KW - SOCP Representability KW - Exact Designs Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-41932 SN - 1438-0064 ER - TY - JOUR A1 - Sagnol, Guillaume A1 - Harman, Radoslav T1 - Computing exact D-optimal designs by mixed integer second-order cone programming JF - The Annals of Statistics N2 - Let the design of an experiment be represented by an $s-$dimensional vector $w$ of weights with nonnegative components. Let the quality of $w$ for the estimation of the parameters of the statistical model be measured by the criterion of $D-$optimality, defined as the $m$th root of the determinant of the information matrix $M(w)=\sum_{i=1}^s w_i A_i A_i^T$, where $A_i$,$i=1,\ldots,s$ are known matrices with $m$ rows. In this paper, we show that the criterion of $D-$optimality is second-order cone representable. As a result, the method of second-order cone programming can be used to compute an approximate $D-$optimal design with any system of linear constraints on the vector of weights. More importantly, the proposed characterization allows us to compute an exact $D-$optimal design, which is possible thanks to high-quality branch-and-cut solvers specialized to solve mixed integer second-order cone programming problems. Our results extend to the case of the criterion of $D_K-$optimality, which measures the quality of $w$ for the estimation of a linear parameter subsystem defined by a full-rank coefficient matrix $K$. We prove that some other widely used criteria are also second-order cone representable, for instance, the criteria of $A-$, $A_K$-, $G-$ and $I-$optimality. We present several numerical examples demonstrating the efficiency and general applicability of the proposed method. We show that in many cases the mixed integer second-order cone programming approach allows us to find a provably optimal exact design, while the standard heuristics systematically miss the optimum. Y1 - 2015 U6 - https://doi.org/10.1214/15-AOS1339 VL - 43 IS - 5 SP - 2198 EP - 2224 ER - TY - GEN A1 - Harman, Radoslav A1 - Sagnol, Guillaume T1 - Computing D-optimal experimental designs for estimating treatment contrasts under the presence of a nuisance time trend N2 - We prove a mathematical programming characterisation of approximate partial D-optimality under general linear constraints. We use this characterisation with a branch-and-bound method to compute a list of all exact D-optimal designs for estimating a pair of treatment contrasts in the presence of a nuisance time trend up to the size of 24 consecutive trials. T3 - ZIB-Report - 15-02 KW - optimal design of experiments KW - nuisance trend KW - branch and bound Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-53640 SN - 1438-0064 ER - TY - CHAP A1 - Sagnol, Guillaume A1 - Harman, Radoslav ED - Steland, Ansgar ED - Rafajłowicz, Ewaryst ED - Szajowski, Krzysztof T1 - Optimal Designs for Steady-state Kalman filters T2 - Stochastic Models, Statistics and Their Applications N2 - We consider a stationary discrete-time linear process that can be observed by a finite number of sensors. The experimental design for the observations consists of an allocation of available resources to these sensors. We formalize the problem of selecting a design that maximizes the information matrix of the steady-state of the Kalman filter, with respect to a standard optimality criterion, such as $D-$ or $A-$optimality. This problem generalizes the optimal experimental design problem for a linear regression model with a finite design space and uncorrelated errors. Finally, we show that under natural assumptions, a steady-state optimal design can be computed by semidefinite programming. Y1 - 2015 U6 - https://doi.org/10.1007/978-3-319-13881-7_17 VL - 122 SP - 149 EP - 157 PB - Springer ER - TY - CHAP A1 - Harman, Radoslav A1 - Sagnol, Guillaume ED - Steland, Ansgar ED - Rafajłowicz, Ewaryst ED - Szajowski, Krzysztof T1 - Computing D-optimal experimental designs for estimating treatment contrasts under the presence of a nuisance time trend T2 - Stochastic Models, Statistics and Their Applications N2 - We prove a mathematical programming characterisation of approximate partial D-optimality under general linear constraints. We use this characterisation with a branch-and-bound method to compute a list of all exact D-optimal designs for estimating a pair of treatment contrasts in the presence of a nuisance time trend up to the size of 24 consecutive trials. Y1 - 2015 U6 - https://doi.org/10.1007/978-3-319-13881-7_10 VL - 122 SP - 83 EP - 91 PB - Springer ER - TY - GEN A1 - Sagnol, Guillaume A1 - Harman, Radoslav T1 - Optimal Designs for Steady-state Kalman filters N2 - We consider a stationary discrete-time linear process that can be observed by a finite number of sensors. The experimental design for the observations consists of an allocation of available resources to these sensors. We formalize the problem of selecting a design that maximizes the information matrix of the steady-state of the Kalman filter, with respect to a standard optimality criterion, such as $D-$ or $A-$optimality. This problem generalizes the optimal experimental design problem for a linear regression model with a finite design space and uncorrelated errors. Finally, we show that under natural assumptions, a steady-state optimal design can be computed by semidefinite programming. T3 - ZIB-Report - 14-39 KW - Optimal Design KW - Kalman Filter KW - Semidefinite programming Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-52808 SN - 1438-0064 ER -