The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform

Christopher J. Martinez - One of the best experts on this subject based on the ideXlab platform.

  • A Novel Solution for Stochastic Dynamic Game of Water Allocation from a Reservoir Using Collocation Method
    Water Resources Management, 2011
    Co-Authors: Mehran Homayounfar, Arman Ganji, Christopher J. Martinez
    Abstract:

    In this study, a continuous model of stochastic dynamic game for water allocation from a reservoir system was developed. The continuous random variable of inflow in the State Transition Function was replaced with a discrete approximant rather than using the mean of the random variable as is done in a continuous model of deterministic dynamic game. As a result, a new solution method was used to solve the stochastic model of game based on collocation method. The collocation method was introduced as an alternative to linear-quadratic (LQ) approximation methods to resolve a dynamic model of game. The collocation method is not limited to the first and second degree approximations, compared to LQ approximation, i.e. Ricatti equations. Furthermore, in spite of LQ related problems, consideration of the stochastic nature of game on the action variables in the collocation method would be possible. The proposed solution method was applied to the real case of reservoir operation, which typically requires considering the effect of uncertainty on decision variables. The results of the solution of the stochastic model of game are compared with the results of a deterministic solution of game, a classical stochastic dynamic programming model (e.g. Bayesian Stochastic Dynamic Programming model, BSDP), and a discrete stochastic dynamic game model (PSDNG). By comparing the results of alternative methods, it is shown that the proposed solution method of stochastic dynamic game is quite capable of providing appropriate reservoir operating policies.

  • A Novel Solution for Stochastic Dynamic Game of Water Allocation from a Reservoir Using Collocation Method
    Water Resources Management, 2011
    Co-Authors: Mehran Homayounfar, Arman Ganji, Christopher J. Martinez
    Abstract:

    In this study, a continuous model of stochastic dynamic game for water allocation from a reservoir system was developed. The continuous random variable of inflow in the State Transition Function was replaced with a discrete approximant rather than using the mean of the random variable as is done in a continuous model of deterministic dynamic game. As a result, a new solution method was used to solve the stochastic model of game based on collocation method. The collocation method was introduced as an alternative to linear-quadratic (LQ) approximation methods to resolve a dynamic model of game. The collocation method is not limited to the first and second degree approximations, compared to LQ approximation, i.e. Ricatti equations. Furthermore, in spite of LQ related problems, consideration of the stochastic nature of game on the action variables in the collocation method would be possible. The proposed solution method was applied to the real case of reservoir operation, which typically requires considering the effect of uncertainty on decision variables. The results of the solution of the stochastic model of game are compared with the results of a deterministic solution of game, a classical stochastic dynamic programming model (e.g. Bayesian Stochastic Dynamic Programming model, BSDP), and a discrete stochastic dynamic game model (PSDNG). By comparing the results of alternative methods, it is shown that the proposed solution method of stochastic dynamic game is quite capable of providing appropriate reservoir operating policies.Mehran Homayounfar & Arman Ganji & C. J. Martine

Mehran Homayounfar - One of the best experts on this subject based on the ideXlab platform.

  • A Novel Solution for Stochastic Dynamic Game of Water Allocation from a Reservoir Using Collocation Method
    Water Resources Management, 2011
    Co-Authors: Mehran Homayounfar, Arman Ganji, Christopher J. Martinez
    Abstract:

    In this study, a continuous model of stochastic dynamic game for water allocation from a reservoir system was developed. The continuous random variable of inflow in the State Transition Function was replaced with a discrete approximant rather than using the mean of the random variable as is done in a continuous model of deterministic dynamic game. As a result, a new solution method was used to solve the stochastic model of game based on collocation method. The collocation method was introduced as an alternative to linear-quadratic (LQ) approximation methods to resolve a dynamic model of game. The collocation method is not limited to the first and second degree approximations, compared to LQ approximation, i.e. Ricatti equations. Furthermore, in spite of LQ related problems, consideration of the stochastic nature of game on the action variables in the collocation method would be possible. The proposed solution method was applied to the real case of reservoir operation, which typically requires considering the effect of uncertainty on decision variables. The results of the solution of the stochastic model of game are compared with the results of a deterministic solution of game, a classical stochastic dynamic programming model (e.g. Bayesian Stochastic Dynamic Programming model, BSDP), and a discrete stochastic dynamic game model (PSDNG). By comparing the results of alternative methods, it is shown that the proposed solution method of stochastic dynamic game is quite capable of providing appropriate reservoir operating policies.

  • A Novel Solution for Stochastic Dynamic Game of Water Allocation from a Reservoir Using Collocation Method
    Water Resources Management, 2011
    Co-Authors: Mehran Homayounfar, Arman Ganji, Christopher J. Martinez
    Abstract:

    In this study, a continuous model of stochastic dynamic game for water allocation from a reservoir system was developed. The continuous random variable of inflow in the State Transition Function was replaced with a discrete approximant rather than using the mean of the random variable as is done in a continuous model of deterministic dynamic game. As a result, a new solution method was used to solve the stochastic model of game based on collocation method. The collocation method was introduced as an alternative to linear-quadratic (LQ) approximation methods to resolve a dynamic model of game. The collocation method is not limited to the first and second degree approximations, compared to LQ approximation, i.e. Ricatti equations. Furthermore, in spite of LQ related problems, consideration of the stochastic nature of game on the action variables in the collocation method would be possible. The proposed solution method was applied to the real case of reservoir operation, which typically requires considering the effect of uncertainty on decision variables. The results of the solution of the stochastic model of game are compared with the results of a deterministic solution of game, a classical stochastic dynamic programming model (e.g. Bayesian Stochastic Dynamic Programming model, BSDP), and a discrete stochastic dynamic game model (PSDNG). By comparing the results of alternative methods, it is shown that the proposed solution method of stochastic dynamic game is quite capable of providing appropriate reservoir operating policies.Mehran Homayounfar & Arman Ganji & C. J. Martine

Arman Ganji - One of the best experts on this subject based on the ideXlab platform.

  • A Novel Solution for Stochastic Dynamic Game of Water Allocation from a Reservoir Using Collocation Method
    Water Resources Management, 2011
    Co-Authors: Mehran Homayounfar, Arman Ganji, Christopher J. Martinez
    Abstract:

    In this study, a continuous model of stochastic dynamic game for water allocation from a reservoir system was developed. The continuous random variable of inflow in the State Transition Function was replaced with a discrete approximant rather than using the mean of the random variable as is done in a continuous model of deterministic dynamic game. As a result, a new solution method was used to solve the stochastic model of game based on collocation method. The collocation method was introduced as an alternative to linear-quadratic (LQ) approximation methods to resolve a dynamic model of game. The collocation method is not limited to the first and second degree approximations, compared to LQ approximation, i.e. Ricatti equations. Furthermore, in spite of LQ related problems, consideration of the stochastic nature of game on the action variables in the collocation method would be possible. The proposed solution method was applied to the real case of reservoir operation, which typically requires considering the effect of uncertainty on decision variables. The results of the solution of the stochastic model of game are compared with the results of a deterministic solution of game, a classical stochastic dynamic programming model (e.g. Bayesian Stochastic Dynamic Programming model, BSDP), and a discrete stochastic dynamic game model (PSDNG). By comparing the results of alternative methods, it is shown that the proposed solution method of stochastic dynamic game is quite capable of providing appropriate reservoir operating policies.

  • A Novel Solution for Stochastic Dynamic Game of Water Allocation from a Reservoir Using Collocation Method
    Water Resources Management, 2011
    Co-Authors: Mehran Homayounfar, Arman Ganji, Christopher J. Martinez
    Abstract:

    In this study, a continuous model of stochastic dynamic game for water allocation from a reservoir system was developed. The continuous random variable of inflow in the State Transition Function was replaced with a discrete approximant rather than using the mean of the random variable as is done in a continuous model of deterministic dynamic game. As a result, a new solution method was used to solve the stochastic model of game based on collocation method. The collocation method was introduced as an alternative to linear-quadratic (LQ) approximation methods to resolve a dynamic model of game. The collocation method is not limited to the first and second degree approximations, compared to LQ approximation, i.e. Ricatti equations. Furthermore, in spite of LQ related problems, consideration of the stochastic nature of game on the action variables in the collocation method would be possible. The proposed solution method was applied to the real case of reservoir operation, which typically requires considering the effect of uncertainty on decision variables. The results of the solution of the stochastic model of game are compared with the results of a deterministic solution of game, a classical stochastic dynamic programming model (e.g. Bayesian Stochastic Dynamic Programming model, BSDP), and a discrete stochastic dynamic game model (PSDNG). By comparing the results of alternative methods, it is shown that the proposed solution method of stochastic dynamic game is quite capable of providing appropriate reservoir operating policies.Mehran Homayounfar & Arman Ganji & C. J. Martine

Danilo P Mandic - One of the best experts on this subject based on the ideXlab platform.

  • Unsupervised State-Space Modeling Using Reproducing Kernels
    IEEE Transactions on Signal Processing, 2015
    Co-Authors: Felipe Tobar, Petar M. Djuric, Danilo P Mandic
    Abstract:

    A novel framework for the design of State-space models (SSMs) is proposed whereby the State-Transition Function of the model is parametrized using reproducing kernels. The nature of SSMs requires learning a latent Function that resides in the State space and for which input-output sample pairs are not available, thus prohibiting the use of gradient-based supervised kernel learning. To this end, we then propose to learn the mixing weights of the kernel estimate by sampling from their posterior density using Monte Carlo methods. We first introduce an offline version of the proposed algorithm, followed by an online version which performs inference on both the parameters and the hidden State through particle filtering. The accuracy of the estimation of the State-Transition Function is first validated on synthetic data. Next, we show that the proposed algorithm outperforms kernel adaptive filters in the prediction of real-world time series, while also providing probabilistic estimates, a key advantage over standard methods.

Jaijeet Roychowdhury - One of the best experts on this subject based on the ideXlab platform.

  • Independent and Interdependent Latch Setup/Hold Time Characterization via Newton–Raphson Solution and Euler Curve Tracking of State-Transition Equations
    IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2008
    Co-Authors: Shweta Srivastava, Jaijeet Roychowdhury
    Abstract:

    Characterizing setup/hold times of latches and registers, which is a task crucial for achieving timing closure of large digital designs, typically occupies months of computation in semiconductor industries. We present a novel approach to speed up latch characterization by formulating the setup/hold time problem as a scalar nonlinear equation ; this nonlinear algebraic formulation is derived from, and embeds within it, the State-Transition Function of the latch. We first present a technique to characterize setup and hold times independently of each other: by decoupling into two equations and and solving each equation using the Newton-Raphson method. Next, we also present a method for interdependent characterization of latch setup/hold times - a core component of techniques for pessimism reduction in timing analysis. We achieve this by solving the underdetermined nonlinear equation using a Moore-Penrose pseudoinverse-based Newton method. Furthermore, we use null-space information from the Newton's Jacobian matrix to efficiently find constant-clock-to- contours (in the setup/hold time plane) via an Euler-Newton curve-tracing procedure. We validate fast convergence and computational advantage for independent characterization on transmission gate and latch/register structures, obtaining speedups of , at high levels of accuracy, over the current standard of binary search. We validate the method for interdependent characterization on true single-phased clock and , obtaining speedups of more than 10 for tracing 17-24 points, over prior approaches while achieving superior accuracy; this speedup linearly increases with the precision with which curve tracing is desired. We also apply our method for interdependent characterization on a transmission gate register to illustrate limitations of our method.

  • DAC - Interdependent latch setup/hold time characterization via Euler-Newton curve tracing on State-Transition equations
    Proceedings of the 44th annual conference on Design automation - DAC '07, 2007
    Co-Authors: Shweta Srivastava, Jaijeet Roychowdhury
    Abstract:

    Interdependent characterization of latch setup/hold times is a core component of techniques for pessimism reduction via Setup/Hold Interdependence Aware Static Timing Analysis (SHIA-STA) [1], [2]. We present an efficient and novel method for such characterization, by formulating the interdependent setup-hold time problem as an underdetermined nonlinear equation h(zetas,zetah) = 0, which we derive from the latch's State-Transition Function. We solve this equation numerically using a Moore-Penrose Newton method. Further, we use null-space information from the Newton's Jacobian matrix to efficiently find constant-clock-to-Q contours (in the setup/hold time plane), via an Euler-Newton curve tracing procedure. We validate the method on TSPC and C2MOS registers, obtaining speedups of more than 20 x over prior approaches while achieving superior accuracy. This speedup increases linearly with the precision with which curve tracing is desired. In view of the importance and large computational expense of latch characterization in industry today, the new technique represents a significant enabling technology for dramatically speeding up industrial timing closure flows.