The Experts below are selected from a list of 132 Experts worldwide ranked by ideXlab platform
Mark Cannon - One of the best experts on this subject based on the ideXlab platform.
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an active set solver for input constrained robust receding horizon control
Conference on Decision and Control, 2011Co-Authors: Johannes Buerger, Mark Cannon, Basil KouvaritakisAbstract:An efficient optimization procedure is proposed for computing a receding horizon control law for linear systems with constrained control inputs and additive disturbances. The procedure uses an active set method to solve the dynamic programming problem associated with the min-max optimization of a Predicted Cost. The active set at the solution is determined at each sampling instant as a function of the current system state using the first-order necessary conditions for optimality. The computational complexity of each iteration is linear in the length of the prediction horizon. We discuss conditions for stability and bounds on state and input l 2 -norms in closed loop operation.
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brief constrained receding horizon predictive control for nonlinear systems
Automatica, 2002Co-Authors: B Kouvaritakis, Mark CannonAbstract:The paper concerns the receding horizon predictive control of constrained nonlinear systems and presents an algorithm which relies on the online solution of a simple linear program (LP). Use is made of a finite control horizon in conjunction with a terminal inequality constraint and a Predicted Cost that includes a terminal penalty term. The optimization procedure is based on predictions made by linearized incremental models at points of a given seed trajectory and the effects of linearization error are taken into account to give a bound on the Predicted tracking error. The algorithm is posed in the form of an LP and the proper selection of the terminal penalty term of the Predicted Cost guarantees the asymptotic stability. The results of the paper are illustrated by means of a simple example.
Mailis Hellenius - One of the best experts on this subject based on the ideXlab platform.
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lifestyle intervention to prevent diabetes in men and women with impaired glucose tolerance is Cost effective
International Journal of Technology Assessment in Health Care, 2007Co-Authors: Peter Lindgren, Jaana Lindstrom, Jaakko Tuomilehto, Matti Uusitupa, Markku Peltonen, Bengt Jonsson, Ulf De Faire, Mailis HelleniusAbstract:Objectives: The Finnish Diabetes Prevention Study (DPS) was a randomized intervention program that evaluated the effect of intensive lifestyle modification on the development of diabetes mellitus type 2 in patients with impaired glucose tolerance. As such, a program is demanding in terms of resources; it is necessary to assess whether it would be money well spent. This determination was the purpose of this study. Methods: We developed a simulation model to assess the economic consequences of an intervention like the one studied in DPS in a Swedish setting. The model used data from the trial itself to assess the effect of intervention on the risk of diabetes and on risk factors for cardiovascular disease. Results from the United Kingdom Prospective Diabetes Study were used to estimate the risk of cardiovascular disease and stroke. Cost data were derived from Swedish studies. The intervention was assumed to be applied to eligible patients from a population-based screening program of 60-year-olds in the County of Stockholm from which the baseline characteristics of the patients was used. Results: The model Predicted that implementing the program would be Cost-saving from the healthcare payers' perspective. Furthermore, it was associated with an increase in estimated survival of .18 years. Taking into consideration the increased consumption by patients due to their longer survival, the Predicted Cost-effectiveness ratio was 2,363€ per quality-adjusted life-year gained. Conclusions: Lifestyle intervention directed toward high-risk subjects would be Cost-saving for the healthcare payer and highly Cost-effective for society as a whole.
Gilbert Litalien - One of the best experts on this subject based on the ideXlab platform.
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the impact of timing and prioritization on the Cost effectiveness of birth cohort testing and treatment for hepatitis c virus in the united states
Hepatology, 2013Co-Authors: Phil Mcewan, T Ward, Yong Yuan, Gilbert LitalienAbstract:Recent United States guidelines recommend one-time birth cohort testing for hepatitis C infection in persons born between 1945 and 1965; this represents a major public health policy undertaking. The purpose of this study was to assess the role of treatment timing and prioritization on Predicted Cost-effectiveness. The MONARCH hepatitis C lifetime simulation model was used in conjunction with a testing and treatment decision tree to estimate the Cost-effectiveness of birth cohort versus risk-based testing incorporating information on age, fibrosis stage and treatment timing. The study used a 1945-1965 birth cohort and included disease progression, testing and treatment-related parameters. Scenario analysis was used to evaluate the impact of hepatitis C virus (HCV) prevalence, treatment eligibility, age, fibrosis stage and timing of treatment initiation on total Costs, quality-adjusted life years (QALYs), HCV-related complications and Cost-effectiveness. The Cost-effectiveness of birth cohort versus risk-based testing was $28,602. Assuming 91% of the population is tested, at least 278,000 people need to be treated for birth cohort testing to maintain Cost-effectiveness. Prioritizing treatment toward those with more advanced fibrosis is associated with a decrease in total Cost of $7.5 billion and 59,035 fewer HCV-related complications. Total QALYs and complications avoided are maximized when treatment initiation occurs as soon as possible after testing. Conclusion: This study confirms that birth cohort testing is, on average, Cost-effective. However, this remains true only when enough tested and HCV-positive subjects are treated to generate sufficient Cost offsets and QALY gains. Given the practical and financial challenges associated with implementing birth cohort testing, the greatest return on investment is obtained when eligible patients are treated immediately and those with more advanced disease are prioritized. (HEPATOLOGY 2013)
B Kouvaritakis - One of the best experts on this subject based on the ideXlab platform.
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brief constrained receding horizon predictive control for nonlinear systems
Automatica, 2002Co-Authors: B Kouvaritakis, Mark CannonAbstract:The paper concerns the receding horizon predictive control of constrained nonlinear systems and presents an algorithm which relies on the online solution of a simple linear program (LP). Use is made of a finite control horizon in conjunction with a terminal inequality constraint and a Predicted Cost that includes a terminal penalty term. The optimization procedure is based on predictions made by linearized incremental models at points of a given seed trajectory and the effects of linearization error are taken into account to give a bound on the Predicted tracking error. The algorithm is posed in the form of an LP and the proper selection of the terminal penalty term of the Predicted Cost guarantees the asymptotic stability. The results of the paper are illustrated by means of a simple example.
S. Keshav - One of the best experts on this subject based on the ideXlab platform.
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REWIRE: An optimization-based framework for unstructured data center network design
2012 Proceedings IEEE INFOCOM, 2012Co-Authors: Andrew R. Curtis, Tommy Carpenter, Mustafa Elsheikh, Alejandro López-ortiz, S. KeshavAbstract:Despite the many proposals for data center network (DCN) architectures, designing a DCN remains challenging. DCN design is especially difficult when expanding an existing network, because traditional DCN design places strict constraints on the topology (e.g., a fat-tree). Recent advances in routing protocols allow data center servers to fully utilize arbitrary networks, so there is no need to require restricted, regular topologies in the data center. Therefore, we propose a data center network design framework, that we call REWIRE, to design networks using an optimization algorithm. Our algorithm finds a network with maximal bisection bandwidth and minimal end-to-end latency while meeting user-defined constraints and accurately modeling the Predicted Cost of the network. We evaluate REWIRE on a wide range of inputs and find that it significantly outperforms previous solutions-its network designs have up to 100-500% more bisection bandwidth and less end-to-end network latency than equivalent-Cost DCNs built with best practices.