The Experts below are selected from a list of 15162 Experts worldwide ranked by ideXlab platform
Yi Zhang - One of the best experts on this subject based on the ideXlab platform.
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note on discounted continuous time markov decision processes with a lower Bounding Function
Journal of Applied Probability, 2017Co-Authors: Xin Guo, Alexei Piunovskiy, Yi ZhangAbstract:We consider the discounted continuous-time Markov decision process (CTMDP), where the negative part of each cost rate is bounded by a drift Function, say w , whereas the positive part is allowed to be arbitrarily unbounded. Our focus is on the existence of a stationary optimal policy for the discounted CTMDP problems out of the more general class. Both constrained and unconstrained problems are considered. Our investigations are based on the continuous-time version of the Veinott transformation. This technique has not been widely employed in the previous literature on CTMDPs, but it clarifies the roles of the imposed conditions in a rather transparent way.
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note on discounted continuous time markov decision processes with a lower Bounding Function
arXiv: Optimization and Control, 2016Co-Authors: Xin Guo, Alexei Piunovskiy, Yi ZhangAbstract:In this paper, we consider the discounted continuous-time Markov decision process (CTMDP) with a lower Bounding Function. In this model, the negative part of each cost rate is bounded by the drift Function, say $w$, whereas the positive part is allowed to be arbitrarily unbounded. Our focus is on the existence of a stationary optimal policy for the discounted CTMDP problems out of the more general class. Both constrained and unconstrained problems are considered. Our investigations are based on a useful transformation for nonhomogeneous Markov pure jump processes that has not yet been widely applied to the study of CTMDPs. This technique was not employed in previous literature, but it clarifies the roles of the imposed conditions in a rather transparent way. As a consequence, we withdraw and weaken several conditions commonly imposed in the literature.
Xin Guo - One of the best experts on this subject based on the ideXlab platform.
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note on discounted continuous time markov decision processes with a lower Bounding Function
Journal of Applied Probability, 2017Co-Authors: Xin Guo, Alexei Piunovskiy, Yi ZhangAbstract:We consider the discounted continuous-time Markov decision process (CTMDP), where the negative part of each cost rate is bounded by a drift Function, say w , whereas the positive part is allowed to be arbitrarily unbounded. Our focus is on the existence of a stationary optimal policy for the discounted CTMDP problems out of the more general class. Both constrained and unconstrained problems are considered. Our investigations are based on the continuous-time version of the Veinott transformation. This technique has not been widely employed in the previous literature on CTMDPs, but it clarifies the roles of the imposed conditions in a rather transparent way.
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note on discounted continuous time markov decision processes with a lower Bounding Function
arXiv: Optimization and Control, 2016Co-Authors: Xin Guo, Alexei Piunovskiy, Yi ZhangAbstract:In this paper, we consider the discounted continuous-time Markov decision process (CTMDP) with a lower Bounding Function. In this model, the negative part of each cost rate is bounded by the drift Function, say $w$, whereas the positive part is allowed to be arbitrarily unbounded. Our focus is on the existence of a stationary optimal policy for the discounted CTMDP problems out of the more general class. Both constrained and unconstrained problems are considered. Our investigations are based on a useful transformation for nonhomogeneous Markov pure jump processes that has not yet been widely applied to the study of CTMDPs. This technique was not employed in previous literature, but it clarifies the roles of the imposed conditions in a rather transparent way. As a consequence, we withdraw and weaken several conditions commonly imposed in the literature.
Alexei Piunovskiy - One of the best experts on this subject based on the ideXlab platform.
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note on discounted continuous time markov decision processes with a lower Bounding Function
Journal of Applied Probability, 2017Co-Authors: Xin Guo, Alexei Piunovskiy, Yi ZhangAbstract:We consider the discounted continuous-time Markov decision process (CTMDP), where the negative part of each cost rate is bounded by a drift Function, say w , whereas the positive part is allowed to be arbitrarily unbounded. Our focus is on the existence of a stationary optimal policy for the discounted CTMDP problems out of the more general class. Both constrained and unconstrained problems are considered. Our investigations are based on the continuous-time version of the Veinott transformation. This technique has not been widely employed in the previous literature on CTMDPs, but it clarifies the roles of the imposed conditions in a rather transparent way.
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note on discounted continuous time markov decision processes with a lower Bounding Function
arXiv: Optimization and Control, 2016Co-Authors: Xin Guo, Alexei Piunovskiy, Yi ZhangAbstract:In this paper, we consider the discounted continuous-time Markov decision process (CTMDP) with a lower Bounding Function. In this model, the negative part of each cost rate is bounded by the drift Function, say $w$, whereas the positive part is allowed to be arbitrarily unbounded. Our focus is on the existence of a stationary optimal policy for the discounted CTMDP problems out of the more general class. Both constrained and unconstrained problems are considered. Our investigations are based on a useful transformation for nonhomogeneous Markov pure jump processes that has not yet been widely applied to the study of CTMDPs. This technique was not employed in previous literature, but it clarifies the roles of the imposed conditions in a rather transparent way. As a consequence, we withdraw and weaken several conditions commonly imposed in the literature.
Pierre Dupont - One of the best experts on this subject based on the ideXlab platform.
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mining a sub matrix of maximal sum
arXiv: Machine Learning, 2017Co-Authors: Vincent Branders, Pierre Schaus, Pierre DupontAbstract:Biclustering techniques have been widely used to identify homogeneous subgroups within large data matrices, such as subsets of genes similarly expressed across subsets of patients. Mining a max-sum sub-matrix is a related but distinct problem for which one looks for a (non-necessarily contiguous) rectangular sub-matrix with a maximal sum of its entries. Le Van et al. (Ranked Tiling, 2014) already illustrated its applicability to gene expression analysis and addressed it with a constraint programming (CP) approach combined with large neighborhood search (CP-LNS). In this work, we exhibit some key properties of this NP-hard problem and define a Bounding Function such that larger problems can be solved in reasonable time. Two different algorithms are proposed in order to exploit the highlighted characteristics of the problem: a CP approach with a global constraint (CPGC) and mixed integer linear programming (MILP). Practical experiments conducted both on synthetic and real gene expression data exhibit the characteristics of these approaches and their relative benefits over the original CP-LNS method. Overall, the CPGC approach tends to be the fastest to produce a good solution. Yet, the MILP formulation is arguably the easiest to formulate and can also be competitive.
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combinatorial optimization algorithms to mine a sub matrix of maximal sum
International Workshop on New Frontiers in Mining Complex Patterns, 2017Co-Authors: Vincent Branders, Pierre Schaus, Pierre DupontAbstract:Biclustering techniques have been widely used to identify homogeneous subgroups within large data matrices, such as subsets of genes similarly expressed across subsets of patients. Mining a max-sum sub-matrix is a related but distinct problem for which one looks for a (non-necessarily contiguous) rectangular sub-matrix with a maximal sum of its entries. Le Van et al. [7] already illustrated its applicability to gene expression analysis and addressed it with a constraint programming (CP) approach combined with large neighborhood search (LNS). In this work, we exhibit some key properties of this \(\mathcal {NP}\)-hard problem and define a Bounding Function such that larger problems can be solved in reasonable time. The use of these properties results in an improved CP-LNS implementation evaluated here. Two additional algorithms are also proposed in order to exploit the highlighted characteristics of the problem: a CP approach with a global constraint (CPGC) and a mixed integer linear programming (MILP). Practical experiments conducted both on synthetic and real gene expression data exhibit the characteristics of these approaches and their relative benefits over the CP-LNS method. Overall, the CPGC approach tends to be the fastest to produce a good solution. Yet, the MILP formulation is arguably the easiest to formulate and can also be competitive.
Vincent Branders - One of the best experts on this subject based on the ideXlab platform.
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mining a sub matrix of maximal sum
arXiv: Machine Learning, 2017Co-Authors: Vincent Branders, Pierre Schaus, Pierre DupontAbstract:Biclustering techniques have been widely used to identify homogeneous subgroups within large data matrices, such as subsets of genes similarly expressed across subsets of patients. Mining a max-sum sub-matrix is a related but distinct problem for which one looks for a (non-necessarily contiguous) rectangular sub-matrix with a maximal sum of its entries. Le Van et al. (Ranked Tiling, 2014) already illustrated its applicability to gene expression analysis and addressed it with a constraint programming (CP) approach combined with large neighborhood search (CP-LNS). In this work, we exhibit some key properties of this NP-hard problem and define a Bounding Function such that larger problems can be solved in reasonable time. Two different algorithms are proposed in order to exploit the highlighted characteristics of the problem: a CP approach with a global constraint (CPGC) and mixed integer linear programming (MILP). Practical experiments conducted both on synthetic and real gene expression data exhibit the characteristics of these approaches and their relative benefits over the original CP-LNS method. Overall, the CPGC approach tends to be the fastest to produce a good solution. Yet, the MILP formulation is arguably the easiest to formulate and can also be competitive.
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combinatorial optimization algorithms to mine a sub matrix of maximal sum
International Workshop on New Frontiers in Mining Complex Patterns, 2017Co-Authors: Vincent Branders, Pierre Schaus, Pierre DupontAbstract:Biclustering techniques have been widely used to identify homogeneous subgroups within large data matrices, such as subsets of genes similarly expressed across subsets of patients. Mining a max-sum sub-matrix is a related but distinct problem for which one looks for a (non-necessarily contiguous) rectangular sub-matrix with a maximal sum of its entries. Le Van et al. [7] already illustrated its applicability to gene expression analysis and addressed it with a constraint programming (CP) approach combined with large neighborhood search (LNS). In this work, we exhibit some key properties of this \(\mathcal {NP}\)-hard problem and define a Bounding Function such that larger problems can be solved in reasonable time. The use of these properties results in an improved CP-LNS implementation evaluated here. Two additional algorithms are also proposed in order to exploit the highlighted characteristics of the problem: a CP approach with a global constraint (CPGC) and a mixed integer linear programming (MILP). Practical experiments conducted both on synthetic and real gene expression data exhibit the characteristics of these approaches and their relative benefits over the CP-LNS method. Overall, the CPGC approach tends to be the fastest to produce a good solution. Yet, the MILP formulation is arguably the easiest to formulate and can also be competitive.