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 - CHAP A1 - Duarte, Belmiro P.M. A1 - Sagnol, Guillaume A1 - Oliveira, Nuno M.C. T1 - A Robust Minimax Semidefinite Programming Formulation for Optimal Design of Experiments for Model Parametrization T2 - 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering N2 - Model-based optimal design of experiments (M-bODE) is a crucial step in model parametrization since it encloses a framework that maximizes the amount of information extracted from a battery of lab experiments. We address the design of M-bODE for dynamic models considering a continuous representation of the design. We use Semidefinite Programming (SDP) to derive robust minmax formulations for nonlinear models, and extend the formulations to other criteria. The approaches are demonstrated for a CSTR where a two-step reaction occurs. Y1 - 2015 U6 - https://doi.org/10.1016/B978-0-444-63578-5.50146-8 VL - 37 SP - 905 EP - 910 ER - TY - JOUR A1 - Duarte, Belmiro P.M. A1 - Sagnol, Guillaume A1 - Wong, Weng Kee T1 - An algorithm based on Semidefinite Programming for finding minimax optimal designs JF - Computational Statistics & Data Analysis Y1 - 2018 U6 - https://doi.org/10.1016/j.csda.2017.09.008 VL - 119 SP - 99 EP - 117 ER - TY - GEN A1 - Duarte, Belmiro P.M. A1 - Sagnol, Guillaume A1 - Oliveira, Nuno M.C. T1 - A robust minimax Semidefinite Programming formulation for optimal design of experiments for model parametrization N2 - Model-based optimal design of experiments (M-bODE) is a crucial step in model parametrization since it encloses a framework that maximizes the amount of information extracted from a battery of lab experiments. We address the design of M-bODE for dynamic models considering a continuous representation of the design. We use Semidefinite Programming (SDP) to derive robust minmax formulations for nonlinear models, and extend the formulations to other criteria. The approaches are demonstrated for a CSTR where a two-step reaction occurs. T3 - ZIB-Report - 15-03 KW - Optimal design of experiments KW - Semidefinite Programming KW - Robust minmax designs Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-54626 SN - 1438-0064 ER - TY - GEN A1 - Sagnol, Guillaume T1 - A Class of Semidefinite Programs with rank-one solutions N2 - We show that a class of semidefinite programs (SDP) admits a solution that is a positive semidefinite matrix of rank at most $r$, where $r$ is the rank of the matrix involved in the objective function of the SDP. The optimization problems of this class are semidefinite packing problems, which are the SDP analogs to vector packing problems. Of particular interest is the case in which our result guarantees the existence of a solution of rank one: we show that the computation of this solution actually reduces to a Second Order Cone Program (SOCP). We point out an application in statistics, in the optimal design of experiments. T3 - ZIB-Report - 11-51 KW - SDP KW - Semidefinite Packing Problem KW - rank 1-solution KW - Low-rank solutions KW - SOCP KW - Optimal Experimental Design KW - Multiresponse experiments Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-14933 SN - 1438-0064 ER - TY - GEN A1 - Sagnol, Guillaume T1 - Network-related problems in Optimal Experimental Design and Second Order Cone Programming N2 - In the past few years several applications of optimal experimental designs have emerged to optimize the measurements in communication networks. The optimal design problems arising from this kind of applications share three interesting properties: (i) measurements are only available at a small number of locations of the network; (ii) each monitor can simultaneously measure several quantities, which can be modeled by ``multiresponse experiments"; (iii) the observation matrices depend on the topology of the network. In this paper, we give an overview of these experimental design problems and recall recent results for the computation of optimal designs by Second Order Cone Programming (SOCP). New results for the network-monitoring of a discrete time process are presented. In particular, we show that the optimal design problem for the monitoring of an AR1 process can be reduced to the standard form and we give experimental results. T3 - ZIB-Report - 11-52 KW - Optimal Experimental Design KW - Second Order Cone Programming KW - Network measurements Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-14942 SN - 1438-0064 VL - 51 IS - 51 SP - 161 EP - 171 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 -