The Experts below are selected from a list of 4251 Experts worldwide ranked by ideXlab platform
N. D. Hari Dass - One of the best experts on this subject based on the ideXlab platform.
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Current status of the numerical simulations of d = 3 SU(2) lattice gauge theory in the Dual Formulation
arXiv: High Energy Physics - Lattice, 2001Co-Authors: N. D. Hari Dass, Dong-shin ShinAbstract:Abstract We have continued our systematic investigations of the numerical simulations of lattice gauge theories in the Dual Formulation. These include: i) a more practical implementation of the quasi-local updating technique, ii) a thorough investigation of the sign problem, iii) issues related to the ergodicity of the various update algorithms, iv) a novel way of measuring conventional observables like plaquette in the Dual formalism and v) investigations of thermalisation. While the Dual Formulation holds out a lot of promises in principle, there are still some ways to go before it can be made into an attractive alternative lattice Formulation.
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Quasi-local update algorithms for numerical simulations of d = 3 SU(2) lattice gauge theory in the Dual Formulation
arXiv: High Energy Physics - Lattice, 2001Co-Authors: N. D. Hari DassAbstract:Abstract In the Dual Formulation of d=3 SU(2) LGT, the link variables are group representations and valid configurations are those satisfying a number of triangle inequalities. In [1] algorithms for local updates that automatically respect these constraints were described. It was also pointed out there that these local updates were not ergodic. In this presentation, we describe two different quasi-local updating algorithms which, in conjunction with the local updates, appear to be ergodic.
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Numerical simulations of d = 3 SU(2) LGT in the Dual Formulation
2000Co-Authors: N. D. Hari DassAbstract:We have developed the techniques necessary for a numerical simulation of d = 3 SU(2) Lattice Gauge theories in the Dual Formulation as originally developed by Anishetty et al [1]. These include updating techniques that preserve the constrained configuration space, efficient evaluation of 6-j symbols and a certain problem associated with the positive indefiniteness of the weight factors.
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Numerical Simulations of d=3 SU(2) LGT in the Dual Formulation
arXiv: High Energy Physics - Lattice, 1999Co-Authors: N. D. Hari DassAbstract:We have developed the techniques necessary for a numerical simulation of d=3 SU(2) Lattice Gauge theories in the Dual Formulation as originally developed by Anishetty et al . These include updating techniques that preserve the constrained configuration space, efficient evaluation of 6-j symbols and a certain problem associated with the positive indefiniteness of the weight factors.
Enlu Zhou - One of the best experts on this subject based on the ideXlab platform.
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Information Relaxation and Dual Formulation of
2020Co-Authors: Fan Ye, Enlu ZhouAbstract:Information relaxation and Duality in Markov deci- sion processes have been studied recently by several researchers with the goal to derive Dual bounds on the value function. In this paper we extend this Dual Formulation to controlled Markov diffusions: in a similar way we relax the constraint that the decision should be made based on the current information and impose a penalty to punish the access to the information in ad- vance. We establish the weak Duality, strong Duality and comple- mentary slackness results in a parallel way as those in Markov decision processes. We further explore the structure of the optimal penalties and expose the connection between the optimal penalties for Markov decision processes and controlled Markov diffusions. We demonstrate the use of this Dual representation in a classic dynamic portfolio choice problem through a new class of penalties, which require little extra computation and produce small Duality gap on the optimal value.
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Dual Formulation of Controlled Markov Diffusions and Its Application
IFAC Proceedings Volumes, 2020Co-Authors: Fan Ye, Enlu ZhouAbstract:Abstract Information relaxation and Duality in Markov decision processes have been studied recently to derive upper bounds on the maximal expected reward (or lower bounds on the minimal expected cost). The idea is to relax the non-anticipativity constraint on the controls and impose a penalty to punish such a violation. In this paper we generalize this Dual approach to controlled Markov diffusions. We develop the weak Duality and strong Duality results, and explore the structure of the optimal penalty. We demonstrate the use of this Dual Formulation by computing upper bounds on the optimal expected utility in a dynamic portfolio choice problem.
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Information Relaxation and Dual Formulation of Controlled Markov Diffusions
IEEE Transactions on Automatic Control, 2015Co-Authors: Fan Ye, Enlu ZhouAbstract:Information relaxation and Duality in Markov decision processes have been studied recently by several researchers with the goal to derive Dual bounds on the value function. In this paper we extend this Dual Formulation to controlled Markov diffusions: in a similar way we relax the constraint that the decision should be made based on the current information and impose a penalty to punish the access to the information in advance. We establish the weak Duality, strong Duality and complementary slackness results in a parallel way as those in Markov decision processes. We further explore the structure of the optimal penalties and expose the connection between the optimal penalties for Markov decision processes and controlled Markov diffusions. We demonstrate the use of this Dual representation in a classic dynamic portfolio choice problem through a new class of penalties, which require little extra computation and produce small Duality gap on the optimal value.
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information relaxation and Dual Formulation of controlled markov diffusions
arXiv: Optimization and Control, 2013Co-Authors: Fan Ye, Enlu ZhouAbstract:Information relaxation and Duality in Markov decision processes have been studied recently by several researchers with the goal to derive Dual bounds on the value function. In this paper we extend this Dual Formulation to controlled Markov diffusions: in a similar way we relax the constraint that the decision should be made based on the current information and impose penalty to punish the access to the information in advance. We establish the weak Duality, strong Duality and complementary slackness results in a parallel way as those in Markov decision processes. We explore the structure of the optimal penalties and expose the connection between Markov decision processes and controlled Markov diffusions. We demonstrate the use of the Dual representation for controlled Markov diffusions in a classic dynamic portfolio choice problem. We evaluate the lower bounds on the expected utility by Monte Carlo simulation under a sub-optimal policy, and we propose a new class of penalties to derive upper bounds with little extra computation. The small gaps between the lower bounds and upper bounds indicate that the available policy is near optimal as well as the effectiveness of our proposed penalty in the Dual method.
Fan Ye - One of the best experts on this subject based on the ideXlab platform.
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Information Relaxation and Dual Formulation of
2020Co-Authors: Fan Ye, Enlu ZhouAbstract:Information relaxation and Duality in Markov deci- sion processes have been studied recently by several researchers with the goal to derive Dual bounds on the value function. In this paper we extend this Dual Formulation to controlled Markov diffusions: in a similar way we relax the constraint that the decision should be made based on the current information and impose a penalty to punish the access to the information in ad- vance. We establish the weak Duality, strong Duality and comple- mentary slackness results in a parallel way as those in Markov decision processes. We further explore the structure of the optimal penalties and expose the connection between the optimal penalties for Markov decision processes and controlled Markov diffusions. We demonstrate the use of this Dual representation in a classic dynamic portfolio choice problem through a new class of penalties, which require little extra computation and produce small Duality gap on the optimal value.
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Dual Formulation of Controlled Markov Diffusions and Its Application
IFAC Proceedings Volumes, 2020Co-Authors: Fan Ye, Enlu ZhouAbstract:Abstract Information relaxation and Duality in Markov decision processes have been studied recently to derive upper bounds on the maximal expected reward (or lower bounds on the minimal expected cost). The idea is to relax the non-anticipativity constraint on the controls and impose a penalty to punish such a violation. In this paper we generalize this Dual approach to controlled Markov diffusions. We develop the weak Duality and strong Duality results, and explore the structure of the optimal penalty. We demonstrate the use of this Dual Formulation by computing upper bounds on the optimal expected utility in a dynamic portfolio choice problem.
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Information Relaxation and Dual Formulation of Controlled Markov Diffusions
IEEE Transactions on Automatic Control, 2015Co-Authors: Fan Ye, Enlu ZhouAbstract:Information relaxation and Duality in Markov decision processes have been studied recently by several researchers with the goal to derive Dual bounds on the value function. In this paper we extend this Dual Formulation to controlled Markov diffusions: in a similar way we relax the constraint that the decision should be made based on the current information and impose a penalty to punish the access to the information in advance. We establish the weak Duality, strong Duality and complementary slackness results in a parallel way as those in Markov decision processes. We further explore the structure of the optimal penalties and expose the connection between the optimal penalties for Markov decision processes and controlled Markov diffusions. We demonstrate the use of this Dual representation in a classic dynamic portfolio choice problem through a new class of penalties, which require little extra computation and produce small Duality gap on the optimal value.
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information relaxation and Dual Formulation of controlled markov diffusions
arXiv: Optimization and Control, 2013Co-Authors: Fan Ye, Enlu ZhouAbstract:Information relaxation and Duality in Markov decision processes have been studied recently by several researchers with the goal to derive Dual bounds on the value function. In this paper we extend this Dual Formulation to controlled Markov diffusions: in a similar way we relax the constraint that the decision should be made based on the current information and impose penalty to punish the access to the information in advance. We establish the weak Duality, strong Duality and complementary slackness results in a parallel way as those in Markov decision processes. We explore the structure of the optimal penalties and expose the connection between Markov decision processes and controlled Markov diffusions. We demonstrate the use of the Dual representation for controlled Markov diffusions in a classic dynamic portfolio choice problem. We evaluate the lower bounds on the expected utility by Monte Carlo simulation under a sub-optimal policy, and we propose a new class of penalties to derive upper bounds with little extra computation. The small gaps between the lower bounds and upper bounds indicate that the available policy is near optimal as well as the effectiveness of our proposed penalty in the Dual method.
Frank Neumann - One of the best experts on this subject based on the ideXlab platform.
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on the use of the Dual Formulation for minimum weighted vertex cover in evolutionary algorithms
Foundations of Genetic Algorithms, 2017Co-Authors: Mojgan Pourhassan, Tobias Friedrich, Frank NeumannAbstract:We consider the weighted minimum vertex cover problem and investigate how its Dual Formulation can be exploited to design evolutionary algorithms that provably obtain a 2-approximation. Investigating multi-valued representations, we show that variants of randomized local search and the (1+1)EA achieve this goal in expected pseudo-polynomial time. In order to speed up the process, we consider the use of step size adaptation in both algorithms and show that RLS obtains a 2-approximation in expected polynomial time while the one+one still encounters a pseudo-polynomial lower bound.
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FOGA - On the Use of the Dual Formulation for Minimum Weighted Vertex Cover in Evolutionary Algorithms
Proceedings of the 14th ACM SIGEVO Conference on Foundations of Genetic Algorithms - FOGA '17, 2017Co-Authors: Mojgan Pourhassan, Tobias Friedrich, Frank NeumannAbstract:We consider the weighted minimum vertex cover problem and investigate how its Dual Formulation can be exploited to design evolutionary algorithms that provably obtain a 2-approximation. Investigating multi-valued representations, we show that variants of randomized local search and the (1+1)EA achieve this goal in expected pseudo-polynomial time. In order to speed up the process, we consider the use of step size adaptation in both algorithms and show that RLS obtains a 2-approximation in expected polynomial time while the one+one still encounters a pseudo-polynomial lower bound.
Myung-gon Yoon - One of the best experts on this subject based on the ideXlab platform.
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Sign-weighted peak minimization problem for continuous-time systems
Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000Co-Authors: Myung-gon YoonAbstract:We study the problem of minimizing a parametrized convex combination of the overshoot and undershoot of SISO continuous time system in response to a known input. From a Dual Formulation we develop a condition for solution existence and specify the structure of optimal solution. In addition, an interrelation between the overshoot and undershoot in controller synthesis is analytically explained in our framework.
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Signed-maximum minimization problem for SISO continuous-time systems
Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171), 1998Co-Authors: Myung-gon Yoon, Ji-yoon KangAbstract:In this paper, we study the problem of minimizing signed (negative or positive) maximum amplitude of a regulated output, due to a fixed input. Like L/sub /spl infin// (l/sub /spl infin//) problems, the optimal performance can be computed from Dual Formulation. However, from the lack of the alignment condition, the optimal solution can not be simply specified, if it exists. We also find suboptimal solutions to this problem and specify its form.
