The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform

Christos G Cassandras - One of the best experts on this subject based on the ideXlab platform.

  • optimal design of personalized prostate cancer therapy using Infinitesimal Perturbation analysis
    Nonlinear Analysis: Hybrid Systems, 2017
    Co-Authors: Julia L Fleck, Christos G Cassandras
    Abstract:

    Abstract The standard treatment for advanced prostate cancer is hormone therapy in the form of continuous androgen suppression (CAS), which unfortunately frequently leads to resistance and relapse. An alternative scheme is intermittent androgen suppression (IAS), in which patients are submitted to cycles of treatment (in the form of androgen deprivation) and off-treatment periods in an alternating manner. In spite of extensive recent clinical experience with IAS, the design of ideal protocols for any given patient remains a challenge. The level of prostate specific antigen (PSA) is frequently monitored to determine when patients will be taken off therapy and when therapy will resume. In this work, we propose a threshold-based policy for optimal IAS therapy design that is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. We use a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under IAS and perform Infinitesimal Perturbation Analysis (IPA) to adaptively adjust PSA threshold values so as to improve therapy outcomes. We also apply this methodology to clinical data from real patients, and obtain promising results and valuable insights for personalized IAS therapy design.

  • Infinitesimal Perturbation analysis for personalized cancer therapy design
    IFAC-PapersOnLine, 2015
    Co-Authors: Julia L Fleck, Christos G Cassandras
    Abstract:

    Abstract We use a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under intermittent androgen suppression (IAS) to study a threshold-based policy for therapy design. IAS is currently one of the most widely used treatments for advanced prostate cancer. Patients undergoing IAS are submitted to cycles of treatment (in the form of androgen deprivation) and off-treatment periods in an alternating manner. One of the main challenges in IAS is to optimally design a therapy scheme, i.e., to determine when to discontinue and recommence androgen suppression. The level of prostate specific antigen (PSA) in a patient's serum is frequently monitored to determine when the patient will be taken off therapy and when therapy will resume. The threshold-based policy we propose is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. Using Infinitesimal Perturbation Analysis (IPA), we derive unbiased gradient estimators of this cost metric with respect to the controllable PSA threshold values based on actual data and show how these estimators can be used to adaptively adjust controllable parameters so as to improve therapy outcomes based on the cost metric defined.

  • ADHS - Infinitesimal Perturbation Analysis for Personalized Cancer Therapy Design
    IFAC-PapersOnLine, 2015
    Co-Authors: Julia L Fleck, Christos G Cassandras
    Abstract:

    Abstract We use a Stochastic Hybrid Automaton (SHA) model of prostate cancer evolution under intermittent androgen suppression (IAS) to study a threshold-based policy for therapy design. IAS is currently one of the most widely used treatments for advanced prostate cancer. Patients undergoing IAS are submitted to cycles of treatment (in the form of androgen deprivation) and off-treatment periods in an alternating manner. One of the main challenges in IAS is to optimally design a therapy scheme, i.e., to determine when to discontinue and recommence androgen suppression. The level of prostate specific antigen (PSA) in a patient's serum is frequently monitored to determine when the patient will be taken off therapy and when therapy will resume. The threshold-based policy we propose is parameterized by lower and upper PSA threshold values and is associated with a cost metric that combines clinically relevant measures of therapy success. Using Infinitesimal Perturbation Analysis (IPA), we derive unbiased gradient estimators of this cost metric with respect to the controllable PSA threshold values based on actual data and show how these estimators can be used to adaptively adjust controllable parameters so as to improve therapy outcomes based on the cost metric defined.

  • Infinitesimal Perturbation analysis for quasi dynamic traffic light controllers
    arXiv: Optimization and Control, 2014
    Co-Authors: Julia L Fleck, Christos G Cassandras
    Abstract:

    We consider the traffic light control problem for a single intersection modeled as a stochastic hybrid system. We study a quasi-dynamic policy based on partial state information defined by detecting whether vehicle backlogs are above or below certain controllable thresholds. Using Infinitesimal Perturbation Analysis (IPA), we derive online gradient estimators of a cost metric with respect to these threshold parameters and use these estimators to iteratively adjust the threshold values through a standard gradient-based algorithm so as to improve overall system performance under various traffic conditions. Results obtained by applying this methodology to a simulated urban setting are also included.

  • WODES - Infinitesimal Perturbation Analysis for Quasi-Dynamic Traffic Light Controllers
    IFAC Proceedings Volumes, 2014
    Co-Authors: Julia L Fleck, Christos G Cassandras
    Abstract:

    Abstract We consider the traffic light control problem for a single intersection modeled as a stochastic hybrid system. We study a quasi-dynamic policy based on partial state information defined by detecting whether vehicle backlogs are above or below certain controllable thresholds. Using Infinitesimal Perturbation Analysis (IPA), we derive online gradient estimators of a cost metric with respect to these threshold parameters and use these estimators to iteratively adjust the threshold values through a standard gradient-based algorithm so as to improve overall system performance under various traffic conditions. Results obtained by applying this methodology to a simulated urban setting are also included.

Christos G. Panayiotou - One of the best experts on this subject based on the ideXlab platform.

  • CDC - Estimating the Critical Density of Road Transportation Networks using Infinitesimal Perturbation Analysis of Hybrid Systems
    2018 IEEE Conference on Decision and Control (CDC), 2018
    Co-Authors: C. Menelaou, Stelios Timotheou, Panayiotis Kolios, Christos G. Panayiotou
    Abstract:

    In this paper, we adobe the Stochastic Fluid Modeling framework to model the critical density of a road network and we employ the route-reservation scheme (as proposed in [1], [2]) to control traffic for a congestion-free operation. To derive the network's critical density value, we employ Infinitesimal Perturbation Analysis (IPA) that provides a stochastic approximation which can be utilized in an on-line fashion to capture the dynamic changes in the critical density value as a consequence of different incidents.

  • Estimating the Critical Density of Road Transportation Networks using Infinitesimal Perturbation Analysis of Hybrid Systems
    2018 IEEE Conference on Decision and Control (CDC), 2018
    Co-Authors: C. Menelaou, Stelios Timotheou, Panayiotis Kolios, Christos G. Panayiotou
    Abstract:

    In this paper, we adobe the Stochastic Fluid Modeling framework to model the critical density of a road network and we employ the route-reservation scheme (as proposed in [1], [2]) to control traffic for a congestion-free operation. To derive the network's critical density value, we employ Infinitesimal Perturbation Analysis (IPA) that provides a stochastic approximation which can be utilized in an on-line fashion to capture the dynamic changes in the critical density value as a consequence of different incidents.

  • On Evaluating SFM-Based Infinitesimal Perturbation Analysis Estimates From Discrete Event System Data
    IEEE Transactions on Automatic Control, 2008
    Co-Authors: Christos G. Panayiotou, M.m. Markou
    Abstract:

    This paper investigates the evaluation of Infinitesimal Perturbation analysis (IPA) estimates that have been derived based on a stochastic fluid model (SFM) using data observed from the sample path of a discrete event system (DES). First, we show that a straightforward implementation of the SFM-based IPA estimates may yield biased estimates when the data are obtained from the actual DES. Then, in order to better approximate the sample path of the DES, we propose a special case of SFM where the arrival and service processes are modeled by piecewise constant on/off sources. The proposed SFM violates some of the assumptions made in [1]-[4] , and, as a result, the sample derivatives no longer exist. However, using the proposed SFM, we obtain the left and right sided sample derivative estimates. As shown in this paper, the sided sample derivatives are much better in approximating the required derivatives compared to the straightforward implementation of the SFM-based IPA estimates.

  • optimization of discrete event system parameters using sfm based Infinitesimal Perturbation analysis estimates
    Conference on Decision and Control, 2007
    Co-Authors: M.m. Markou, Christos G. Panayiotou
    Abstract:

    This paper deals with the problem of optimizing the performance of a discrete event system (DES) using Infinitesimal Perturbation analysis (IPA) estimates obtained from a stochastic fluid model (SFM). In order to better approximate the behavior of the DES, we propose a special case of SFM where the arrival and service processes are modeled by piecewise constant ON/OFF sources. The proposed SFM however violates some of the assumptions made in Cassandras, C. G., et al (2002) and as a result the sample derivatives no longer exist. However, using the proposed SFM, we obtain the left and right sided sample derivative estimates. This paper investigates the implementation of various IPA estimates that have been derived based on a stochastic fluid model (SFM) for the optimization of parameters of a discrete event system (DES). In this paper we investigate gradient based and subgradient optimization methods. As shown in this paper, for many scenarios all algorithms have comparable results however, in some cases subgradient optimization produces better results.

  • CDC - Optimization of discrete event system parameters using SFM-based Infinitesimal Perturbation analysis estimates
    2007 46th IEEE Conference on Decision and Control, 2007
    Co-Authors: M.m. Markou, Christos G. Panayiotou
    Abstract:

    This paper deals with the problem of optimizing the performance of a discrete event system (DES) using Infinitesimal Perturbation analysis (IPA) estimates obtained from a stochastic fluid model (SFM). In order to better approximate the behavior of the DES, we propose a special case of SFM where the arrival and service processes are modeled by piecewise constant ON/OFF sources. The proposed SFM however violates some of the assumptions made in Cassandras, C. G., et al (2002) and as a result the sample derivatives no longer exist. However, using the proposed SFM, we obtain the left and right sided sample derivative estimates. This paper investigates the implementation of various IPA estimates that have been derived based on a stochastic fluid model (SFM) for the optimization of parameters of a discrete event system (DES). In this paper we investigate gradient based and subgradient optimization methods. As shown in this paper, for many scenarios all algorithms have comparable results however, in some cases subgradient optimization produces better results.

Carla Seatzu - One of the best experts on this subject based on the ideXlab platform.

  • performance regulation of event driven dynamical systems using Infinitesimal Perturbation analysis
    Nonlinear Analysis: Hybrid Systems, 2016
    Co-Authors: Yorai Wardi, Carla Seatzu, X Chen, Sudhakar Yalamanchili
    Abstract:

    Abstract This paper presents a performance-regulation method for a class of stochastic timed event-driven systems aimed at output tracking of a given reference setpoint. The systems are either Discrete Event Dynamic Systems (DEDS) such as queueing networks or Petri nets, or Hybrid Systems (HS) with time-driven dynamics and event-driven dynamics, like fluid queues and hybrid Petri nets. The regulator, designed for simplicity and speed of computation, is comprised of a single integrator having a variable gain to ensure effective tracking under time-varying plants. The gain’s computation is based on the Infinitesimal Perturbation Analysis (IPA) gradient of the plant function with respect to the control variable, and the resultant tracking can be quite robust with respect to modeling inaccuracies and gradient-estimation errors. The proposed technique is tested on examples taken from various application areas and modeled with different formalisms, including queueing models, Petri-net model of a production-inventory control system, and a stochastic DEDS model of a multicore chip control. Simulation results are presented in support of the proposed approach.

  • Performance regulation of event-driven dynamical systems using Infinitesimal Perturbation analysis ☆
    Nonlinear Analysis: Hybrid Systems, 2016
    Co-Authors: Yorai Wardi, Carla Seatzu, X Chen, Sudhakar Yalamanchili
    Abstract:

    Abstract This paper presents a performance-regulation method for a class of stochastic timed event-driven systems aimed at output tracking of a given reference setpoint. The systems are either Discrete Event Dynamic Systems (DEDS) such as queueing networks or Petri nets, or Hybrid Systems (HS) with time-driven dynamics and event-driven dynamics, like fluid queues and hybrid Petri nets. The regulator, designed for simplicity and speed of computation, is comprised of a single integrator having a variable gain to ensure effective tracking under time-varying plants. The gain’s computation is based on the Infinitesimal Perturbation Analysis (IPA) gradient of the plant function with respect to the control variable, and the resultant tracking can be quite robust with respect to modeling inaccuracies and gradient-estimation errors. The proposed technique is tested on examples taken from various application areas and modeled with different formalisms, including queueing models, Petri-net model of a production-inventory control system, and a stochastic DEDS model of a multicore chip control. Simulation results are presented in support of the proposed approach.

  • congestion management in traffic light intersections via Infinitesimal Perturbation analysis
    arXiv: Optimization and Control, 2015
    Co-Authors: Carla Seatzu, Yorai Wardi
    Abstract:

    We present a flow-control technique in traffic-light intersections, aiming at regulating queue lengths to given reference setpoints. The technique is based on multivariable integrators with adaptive gains, computed at each control cycle by assessing the IPA gradients of the plant functions. Moreover, the IPA gradients are computable on-line despite the absence of detailed models of the traffic flows. The technique is applied to a two-intersection system where it exhibits robustness with respect to modeling uncertainties and computing errors, thereby permitting us to simplify the on-line computations perhaps at the expense of accuracy while achieving the desired tracking. We compare, by simulation, the performance of a centralized, joint two-intersection control with distributed control of each intersection separately, and show similar performance of the two control schemes for a range of parameters.

  • congestion management in traffic light intersections via Infinitesimal Perturbation analysis
    IFAC-PapersOnLine, 2015
    Co-Authors: Carla Seatzu, Yorai Wardi
    Abstract:

    Abstract We present a flow-control technique in traffic-light intersections, aiming at regulating queue lengths to given setpoint references. The technique is based on multivariable integrators with adaptive gains, computed at each control cycle by assessing the IPA gradients of the plant functions. Moreover, the IPA gradients are computable on-line despite the absence of detailed models of the traffic flows. The technique is applied to a two-intersection system where it exhibits robustness with respect to modeling uncertainties and computing errors, thereby permitting us to simplify the on-line computations at the expense of accuracy while achieving the desired tracking. We compare, by simulation, the performance of a centralized, joint two-intersection control with distributed control of each intersection separately, and show similar performance of the two control schemes for a range of parameters.

  • Infinitesimal Perturbation Analysis of Stochastic Hybrid Systems: Application to Congestion Management in Traffic-Light Intersections
    arXiv: Optimization and Control, 2014
    Co-Authors: Yorai Wardi, Carla Seatzu
    Abstract:

    This paper presents a new approach to congestion management at traffic-light intersections. The approach is based on controlling the relative lengths of red/green cycles in order to have the congestion level track a given reference. It uses an integral control with adaptive gains, designed to provide fast tracking and wide stability margins. The gains are inverse-proportional to the derivative of the plant-function with respect to the control parameter, and are computed by Infinitesimal Perturbation analysis. Convergence of this technique is shown to be robust with respect to modeling uncertainties, computing errors, and other random effects. The framework is presented in the setting of stochastic hybrid systems, and applied to a particular traffic-light model. This is but an initial study and hence the latter model is simple, but it captures some of the salient features of traffic-light processes. The paper concludes with comments on possible extensions of the proposed approach to traffic-light grids with realistic flow models.

Yorai Wardi - One of the best experts on this subject based on the ideXlab platform.

  • performance regulation of event driven dynamical systems using Infinitesimal Perturbation analysis
    Nonlinear Analysis: Hybrid Systems, 2016
    Co-Authors: Yorai Wardi, Carla Seatzu, X Chen, Sudhakar Yalamanchili
    Abstract:

    Abstract This paper presents a performance-regulation method for a class of stochastic timed event-driven systems aimed at output tracking of a given reference setpoint. The systems are either Discrete Event Dynamic Systems (DEDS) such as queueing networks or Petri nets, or Hybrid Systems (HS) with time-driven dynamics and event-driven dynamics, like fluid queues and hybrid Petri nets. The regulator, designed for simplicity and speed of computation, is comprised of a single integrator having a variable gain to ensure effective tracking under time-varying plants. The gain’s computation is based on the Infinitesimal Perturbation Analysis (IPA) gradient of the plant function with respect to the control variable, and the resultant tracking can be quite robust with respect to modeling inaccuracies and gradient-estimation errors. The proposed technique is tested on examples taken from various application areas and modeled with different formalisms, including queueing models, Petri-net model of a production-inventory control system, and a stochastic DEDS model of a multicore chip control. Simulation results are presented in support of the proposed approach.

  • Performance regulation of event-driven dynamical systems using Infinitesimal Perturbation analysis ☆
    Nonlinear Analysis: Hybrid Systems, 2016
    Co-Authors: Yorai Wardi, Carla Seatzu, X Chen, Sudhakar Yalamanchili
    Abstract:

    Abstract This paper presents a performance-regulation method for a class of stochastic timed event-driven systems aimed at output tracking of a given reference setpoint. The systems are either Discrete Event Dynamic Systems (DEDS) such as queueing networks or Petri nets, or Hybrid Systems (HS) with time-driven dynamics and event-driven dynamics, like fluid queues and hybrid Petri nets. The regulator, designed for simplicity and speed of computation, is comprised of a single integrator having a variable gain to ensure effective tracking under time-varying plants. The gain’s computation is based on the Infinitesimal Perturbation Analysis (IPA) gradient of the plant function with respect to the control variable, and the resultant tracking can be quite robust with respect to modeling inaccuracies and gradient-estimation errors. The proposed technique is tested on examples taken from various application areas and modeled with different formalisms, including queueing models, Petri-net model of a production-inventory control system, and a stochastic DEDS model of a multicore chip control. Simulation results are presented in support of the proposed approach.

  • congestion management in traffic light intersections via Infinitesimal Perturbation analysis
    arXiv: Optimization and Control, 2015
    Co-Authors: Carla Seatzu, Yorai Wardi
    Abstract:

    We present a flow-control technique in traffic-light intersections, aiming at regulating queue lengths to given reference setpoints. The technique is based on multivariable integrators with adaptive gains, computed at each control cycle by assessing the IPA gradients of the plant functions. Moreover, the IPA gradients are computable on-line despite the absence of detailed models of the traffic flows. The technique is applied to a two-intersection system where it exhibits robustness with respect to modeling uncertainties and computing errors, thereby permitting us to simplify the on-line computations perhaps at the expense of accuracy while achieving the desired tracking. We compare, by simulation, the performance of a centralized, joint two-intersection control with distributed control of each intersection separately, and show similar performance of the two control schemes for a range of parameters.

  • congestion management in traffic light intersections via Infinitesimal Perturbation analysis
    IFAC-PapersOnLine, 2015
    Co-Authors: Carla Seatzu, Yorai Wardi
    Abstract:

    Abstract We present a flow-control technique in traffic-light intersections, aiming at regulating queue lengths to given setpoint references. The technique is based on multivariable integrators with adaptive gains, computed at each control cycle by assessing the IPA gradients of the plant functions. Moreover, the IPA gradients are computable on-line despite the absence of detailed models of the traffic flows. The technique is applied to a two-intersection system where it exhibits robustness with respect to modeling uncertainties and computing errors, thereby permitting us to simplify the on-line computations at the expense of accuracy while achieving the desired tracking. We compare, by simulation, the performance of a centralized, joint two-intersection control with distributed control of each intersection separately, and show similar performance of the two control schemes for a range of parameters.

  • Infinitesimal Perturbation Analysis of Stochastic Hybrid Systems: Application to Congestion Management in Traffic-Light Intersections
    arXiv: Optimization and Control, 2014
    Co-Authors: Yorai Wardi, Carla Seatzu
    Abstract:

    This paper presents a new approach to congestion management at traffic-light intersections. The approach is based on controlling the relative lengths of red/green cycles in order to have the congestion level track a given reference. It uses an integral control with adaptive gains, designed to provide fast tracking and wide stability margins. The gains are inverse-proportional to the derivative of the plant-function with respect to the control parameter, and are computed by Infinitesimal Perturbation analysis. Convergence of this technique is shown to be robust with respect to modeling uncertainties, computing errors, and other random effects. The framework is presented in the setting of stochastic hybrid systems, and applied to a particular traffic-light model. This is but an initial study and hence the latter model is simple, but it captures some of the salient features of traffic-light processes. The paper concludes with comments on possible extensions of the proposed approach to traffic-light grids with realistic flow models.

Richelle V Adams - One of the best experts on this subject based on the ideXlab platform.

  • Infinitesimal Perturbation analysis of a single-stage fluid queue with loss feedback and non-responsive competing traffic
    Discrete Event Dynamic Systems, 2014
    Co-Authors: Richelle V Adams
    Abstract:

    In this paper we perform Infinitesimal Perturbation Analysis (IPA) for a single-stage stochastic fluid queue that is shared between two competing sources, one that employs additive loss-feedback congestion control and the other that employs no congestion-control (i.e., it is unresponsive). This scenario is applicable within the realm of computer communication networks particularly at bottleneck router queues where multiple and diverse flows compete for bandwidth. We optimize the tradeoff between total loss volume and queue workload (a measure for queueing delay). Although a sound knowledge of the system's dynamics is required to derive the IPA gradient estimators, no knowledge of the underlying probability distributions governing the system is required. What results are fairly simple counting processes, whose values can be computed directly from an ongoing live stream of traffic.

  • Infinitesimal Perturbation analysis of a multi-stage tandem of fluid queue with additive loss feedback
    Systems & Control Letters, 2014
    Co-Authors: Richelle V Adams
    Abstract:

    Abstract In this paper Infinitesimal Perturbation Analysis (IPA) is used to derive the gradient estimators for loss volume and queue workload in a multi-stage tandem of stochastic fluid queues with instantaneous additive loss-feedback for overall congestion control. These gradient estimators are then used to drive a standard stochastic approximation algorithm to optimize, with respect to the buffer limits of the individual queues, an objective function which is the weighted sum of loss volume and queue workload of the queues that make up the tandem.

  • a unified approach to Infinitesimal Perturbation analysis in stochastic flow models the single stage case
    IEEE Transactions on Automatic Control, 2010
    Co-Authors: Y Wardi, Richelle V Adams, Benjamin Melamed
    Abstract:

    This paper develops an abstract framework for Infinitesimal Perturbation Analysis (IPA) in the setting of stochastic flow models, and it applies it to several problems arising in the study of flow control in single-server fluid-flow queues. The framework is based on a switched-mode hybrid-system paradigm, and especially on the interplay between its discrete-event dynamics and continuous-time dynamics. It is quite general, and most of the formulas obtained to-date for IPA on single-server queues can be derived from it as simple corollaries. Additional new results can be derived as well, and the paper demonstrates it by considering a queue with loss-rate-based flow control. The main contribution of the paper is in the proposed framework and its apparent broad scope. Its possible extension to a general class of fluid-flow queueing networks appears likely, and will be pointed out as a direction for future research.