The Experts below are selected from a list of 60882 Experts worldwide ranked by ideXlab platform
Raymond A Decarlo - One of the best experts on this subject based on the ideXlab platform.
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A Comparison of the Embedding Method With Multiparametric Programming, Mixed-Integer Programming, Gradient-Descent, and Hybrid Minimum Principle-Based Methods
IEEE Transactions on Control Systems Technology, 2014Co-Authors: Richard T. Meyer, Milos Zefran, Raymond A DecarloAbstract:In recent years, the Embedding approach for solving switched optimal control problems has been developed in a series of papers. However, the Embedding approach, which advantageously converts the hybrid optimal control problem to a classical nonlinear optimization, has not been extensively compared with alternative solution approaches. The goal of this paper is thus to compare the Embedding approach with multiparametric programming, mixed-integer programming [mixed integer programming (MIP), commercial (CPLEX)], and gradient-descent-based Methods in the context of five recently published examples: 1) a spring-mass system; 2) moving-target tracking for a mobile robot; 3) two-tank filling; dc-dc boost converter; and 5) skid-steered vehicle. A sixth example, an autonomous switched 11-region linear system, is used to compare a hybrid minimum principle Method and traditional numerical programming. For a given performance index (PI) for each case, cost and solution times are presented. It is shown that there are numerical advantages of the Embedding approach: lower PI cost (except in some instances when autonomous switches are present), generally faster solution time, and convergence to a solution when other Methods may fail. In addition, the Embedding Method requires no ad hoc assumptions (e.g., predetermined mode sequences) or specialized control models. Theoretical advantages of the Embedding approach over the other Methods are also described; guaranteed existence of a solution under mild conditions, convexity of the embedded hybrid optimization problem (under the customary conditions on the PI), solvability with traditional techniques (e.g., sequential quadratic programming) avoiding the combinatorial complexity in the number of modes/discrete variables of MIP, applicability to affine nonlinear systems, and no need to explicitly assign discrete/mode variables to autonomous switches. Finally, common misconcept- ons regarding the Embedding approach are addressed, including whether it uses an average value control model (no), whether it is necessary to tweak the algorithm to obtain bang-bang solutions (no), whether it requires infinite switching to implement embedded solution (no), and whether it has real-time capability (yes).
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a comparison of the Embedding Method to multi parametric programming mixed integer programming gradient descent and hybrid minimum principle based Methods
arXiv: Optimization and Control, 2012Co-Authors: Richard Meyer, Milos Zefran, Raymond A DecarloAbstract:In recent years, the Embedding approach for solving switched optimal control problems has been developed in a series of papers. However, the Embedding approach, which advantageously converts the hybrid optimal control problem to a classical nonlinear optimization, has not been extensively compared to alternative solution approaches. The goal of this paper is thus to compare the Embedding approach to multi-parametric programming, mixed-integer programming (e.g., CPLEX), and gradient-descent based Methods in the context of five recently published examples: a spring-mass system, moving-target tracking for a mobile robot, two-tank filling, DC-DC boost converter, and skid-steered vehicle. A sixth example, an autonomous switched 11-region linear system, is used to compare a hybrid minimum principle Method and traditional numerical programming. For a given performance index for each case, cost and solution times are presented. It is shown that there are numerical advantages of the Embedding approach: lower performance index cost (except in some instances when autonomous switches are present), generally faster solution time, and convergence to a solution when other Methods may fail. In addition, the Embedding Method requires no ad hoc assumptions (e.g., predetermined mode sequences) or specialized control models. Theoretical advantages of the Embedding approach over the other Methods are also described: guaranteed existence of a solution under mild conditions, convexity of the embedded hybrid optimization problem (under the customary conditions on the performance index), solvability with traditional techniques (e.g., sequential quadratic programming) avoiding the combinatorial complexity in the number of modes/discrete variables of mixed-integer programming, applicability to affine nonlinear systems, and no need to explicitly assign discrete/mode variables to autonomous switches.
Bin Wang - One of the best experts on this subject based on the ideXlab platform.
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sbert wk a sentence Embedding Method by dissecting bert based word models
IEEE Transactions on Audio Speech and Language Processing, 2020Co-Authors: Bin Wang, C Jay C KuoAbstract:Sentence Embedding is an important research topic in natural language processing (NLP) since it can transfer knowledge to downstream tasks. Meanwhile, a contextualized word representation, called BERT, achieves the state-of-the-art performance in quite a few NLP tasks. Yet, it is an open problem to generate a high quality sentence representation from BERT-based word models. It was shown in previous study that different layers of BERT capture different linguistic properties. This allows us to fuse information across layers to find better sentence representations. In this work, we study the layer-wise pattern of the word representation of deep contextualized models. Then, we propose a new sentence Embedding Method by dissecting BERT-based word models through geometric analysis of the space spanned by the word representation. It is called the SBERT-WK Method. No further training is required in SBERT-WK. We evaluate SBERT-WK on semantic textual similarity and downstream supervised tasks. Furthermore, ten sentence-level probing tasks are presented for detailed linguistic analysis. Experiments show that SBERT-WK achieves the state-of-the-art performance. Our codes are publicly available.
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sbert wk a sentence Embedding Method by dissecting bert based word models
arXiv: Computation and Language, 2020Co-Authors: Bin Wang, C Jay C KuoAbstract:Sentence Embedding is an important research topic in natural language processing (NLP) since it can transfer knowledge to downstream tasks. Meanwhile, a contextualized word representation, called BERT, achieves the state-of-the-art performance in quite a few NLP tasks. Yet, it is an open problem to generate a high quality sentence representation from BERT-based word models. It was shown in previous study that different layers of BERT capture different linguistic properties. This allows us to fusion information across layers to find better sentence representation. In this work, we study the layer-wise pattern of the word representation of deep contextualized models. Then, we propose a new sentence Embedding Method by dissecting BERT-based word models through geometric analysis of the space spanned by the word representation. It is called the SBERT-WK Method. No further training is required in SBERT-WK. We evaluate SBERT-WK on semantic textual similarity and downstream supervised tasks. Furthermore, ten sentence-level probing tasks are presented for detailed linguistic analysis. Experiments show that SBERT-WK achieves the state-of-the-art performance. Our codes are publicly available.
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multi stage holomorphic Embedding Method for calculating the power voltage curve
IEEE Transactions on Power Systems, 2018Co-Authors: Bin Wang, Chengxi Liu, Kai SunAbstract:The recently proposed non-iterative load flow Method, called the holomorphic Embedding Method, may encounter the precision issue, i.e., the nontrivial round-off errors caused by the limited digits used in computing the power-voltage (P-V) curve for a heavily loaded power system. This letter proposes a multi-stage scheme to solve such a precision issue and calculate an accurate P-V curve. The scheme is verified on the New England 39-bus power system and benchmarked with the result from the traditional continuation power flow Method.
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multi stage holomorphic Embedding Method for calculating the power voltage curve
arXiv: Systems and Control, 2017Co-Authors: Bin Wang, Chengxi Liu, Kai SunAbstract:The recently proposed non-iterative load flow Method, called the holomorphic Embedding Method, may encounter the precision issue, i.e. nontrivial round-off errors caused by the limit of digits used in computation when calculating the power-voltage (P-V) curve for a heavily loaded power system. This letter proposes a multi-stage scheme to solve such a precision issue and calculate an accurate P-V curve. The scheme is verified on the New Eng-land 39-bus power system and benchmarked with the result from the traditional continuation power flow Method.
Richard T. Meyer - One of the best experts on this subject based on the ideXlab platform.
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A Comparison of the Embedding Method With Multiparametric Programming, Mixed-Integer Programming, Gradient-Descent, and Hybrid Minimum Principle-Based Methods
IEEE Transactions on Control Systems Technology, 2014Co-Authors: Richard T. Meyer, Milos Zefran, Raymond A DecarloAbstract:In recent years, the Embedding approach for solving switched optimal control problems has been developed in a series of papers. However, the Embedding approach, which advantageously converts the hybrid optimal control problem to a classical nonlinear optimization, has not been extensively compared with alternative solution approaches. The goal of this paper is thus to compare the Embedding approach with multiparametric programming, mixed-integer programming [mixed integer programming (MIP), commercial (CPLEX)], and gradient-descent-based Methods in the context of five recently published examples: 1) a spring-mass system; 2) moving-target tracking for a mobile robot; 3) two-tank filling; dc-dc boost converter; and 5) skid-steered vehicle. A sixth example, an autonomous switched 11-region linear system, is used to compare a hybrid minimum principle Method and traditional numerical programming. For a given performance index (PI) for each case, cost and solution times are presented. It is shown that there are numerical advantages of the Embedding approach: lower PI cost (except in some instances when autonomous switches are present), generally faster solution time, and convergence to a solution when other Methods may fail. In addition, the Embedding Method requires no ad hoc assumptions (e.g., predetermined mode sequences) or specialized control models. Theoretical advantages of the Embedding approach over the other Methods are also described; guaranteed existence of a solution under mild conditions, convexity of the embedded hybrid optimization problem (under the customary conditions on the PI), solvability with traditional techniques (e.g., sequential quadratic programming) avoiding the combinatorial complexity in the number of modes/discrete variables of MIP, applicability to affine nonlinear systems, and no need to explicitly assign discrete/mode variables to autonomous switches. Finally, common misconcept- ons regarding the Embedding approach are addressed, including whether it uses an average value control model (no), whether it is necessary to tweak the algorithm to obtain bang-bang solutions (no), whether it requires infinite switching to implement embedded solution (no), and whether it has real-time capability (yes).
Zoila Barandiaran - One of the best experts on this subject based on the ideXlab platform.
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parallel linear scaling building block and Embedding Method based on localized orbitals and orbital specific basis sets
Journal of Chemical Physics, 2004Co-Authors: Luis Seijo, Zoila BarandiaranAbstract:We present a linear scaling Method for the energy minimization step of semiempirical and first-principles Hartree–Fock and Kohn–Sham calculations. It is based on the self-consistent calculation of the optimum localized orbitals of any localization Method of choice and on the use of orbital-specific basis sets. The full set of localized orbitals of a large molecule is seen as an orbital mosaic where each tessera is made of only a few of them. The orbital tesserae are computed out of a set of embedded cluster pseudoeigenvalue coupled equations which are solved in a building-block self-consistent fashion. In each iteration, the embedded cluster equations are solved independently of each other and, as a result, the Method is parallel at a high level of the calculation. In addition to full system calculations, the Method enables to perform simpler, much less demanding embedded cluster calculations, where only a fraction of the localized molecular orbitals are variational while the rest is frozen, taking advantage of the transferability of the localized orbitals of a given localization Method between similar molecules. Monitoring single point energy calculations of large poly(ethylene oxide) molecules and three dimensional carbon monoxide clusters using an extended Huckel Hamiltonian are presented.
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parallel linear scaling building block and Embedding Method based on localized orbitals and orbital specific basis sets
arXiv: Materials Science, 2004Co-Authors: Luis Seijo, Zoila BarandiaranAbstract:We present a new linear scaling Method for the energy minimization step of semiempirical and first-principles Hartree-Fock and Kohn-Sham calculations. It is based on the self-consistent calculation of the optimum localized orbitals of any localization Method of choice and on the use of orbital-specific basis sets. The full set of localized orbitals of a large molecule is seen as an orbital mosaic where each tessera is made of only a few of them. The orbital tesserae are computed out of a set of embedded cluster pseudoeigenvalue coupled equations which are solved in a building-block self-consistent fashion. In each iteration, the embedded cluster equations are solved independently of each other and, as a result, the Method is parallel at a high level of the calculation. In addition to full system calculations, the Method enables to perform simpler, much less demanding embedded cluster calculations, where only a fraction of the localized molecular orbitals are variational while the rest are frozen, taking advantage of the transferability of the localized orbitals of a given localization Method between similar molecules. Monitoring single point energy calculations of large poly(ethylene oxide) molecules and three dimensional carbon monoxide clusters using an extended Huckel Hamiltonian are presented.
Milos Zefran - One of the best experts on this subject based on the ideXlab platform.
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A Comparison of the Embedding Method With Multiparametric Programming, Mixed-Integer Programming, Gradient-Descent, and Hybrid Minimum Principle-Based Methods
IEEE Transactions on Control Systems Technology, 2014Co-Authors: Richard T. Meyer, Milos Zefran, Raymond A DecarloAbstract:In recent years, the Embedding approach for solving switched optimal control problems has been developed in a series of papers. However, the Embedding approach, which advantageously converts the hybrid optimal control problem to a classical nonlinear optimization, has not been extensively compared with alternative solution approaches. The goal of this paper is thus to compare the Embedding approach with multiparametric programming, mixed-integer programming [mixed integer programming (MIP), commercial (CPLEX)], and gradient-descent-based Methods in the context of five recently published examples: 1) a spring-mass system; 2) moving-target tracking for a mobile robot; 3) two-tank filling; dc-dc boost converter; and 5) skid-steered vehicle. A sixth example, an autonomous switched 11-region linear system, is used to compare a hybrid minimum principle Method and traditional numerical programming. For a given performance index (PI) for each case, cost and solution times are presented. It is shown that there are numerical advantages of the Embedding approach: lower PI cost (except in some instances when autonomous switches are present), generally faster solution time, and convergence to a solution when other Methods may fail. In addition, the Embedding Method requires no ad hoc assumptions (e.g., predetermined mode sequences) or specialized control models. Theoretical advantages of the Embedding approach over the other Methods are also described; guaranteed existence of a solution under mild conditions, convexity of the embedded hybrid optimization problem (under the customary conditions on the PI), solvability with traditional techniques (e.g., sequential quadratic programming) avoiding the combinatorial complexity in the number of modes/discrete variables of MIP, applicability to affine nonlinear systems, and no need to explicitly assign discrete/mode variables to autonomous switches. Finally, common misconcept- ons regarding the Embedding approach are addressed, including whether it uses an average value control model (no), whether it is necessary to tweak the algorithm to obtain bang-bang solutions (no), whether it requires infinite switching to implement embedded solution (no), and whether it has real-time capability (yes).
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a comparison of the Embedding Method to multi parametric programming mixed integer programming gradient descent and hybrid minimum principle based Methods
arXiv: Optimization and Control, 2012Co-Authors: Richard Meyer, Milos Zefran, Raymond A DecarloAbstract:In recent years, the Embedding approach for solving switched optimal control problems has been developed in a series of papers. However, the Embedding approach, which advantageously converts the hybrid optimal control problem to a classical nonlinear optimization, has not been extensively compared to alternative solution approaches. The goal of this paper is thus to compare the Embedding approach to multi-parametric programming, mixed-integer programming (e.g., CPLEX), and gradient-descent based Methods in the context of five recently published examples: a spring-mass system, moving-target tracking for a mobile robot, two-tank filling, DC-DC boost converter, and skid-steered vehicle. A sixth example, an autonomous switched 11-region linear system, is used to compare a hybrid minimum principle Method and traditional numerical programming. For a given performance index for each case, cost and solution times are presented. It is shown that there are numerical advantages of the Embedding approach: lower performance index cost (except in some instances when autonomous switches are present), generally faster solution time, and convergence to a solution when other Methods may fail. In addition, the Embedding Method requires no ad hoc assumptions (e.g., predetermined mode sequences) or specialized control models. Theoretical advantages of the Embedding approach over the other Methods are also described: guaranteed existence of a solution under mild conditions, convexity of the embedded hybrid optimization problem (under the customary conditions on the performance index), solvability with traditional techniques (e.g., sequential quadratic programming) avoiding the combinatorial complexity in the number of modes/discrete variables of mixed-integer programming, applicability to affine nonlinear systems, and no need to explicitly assign discrete/mode variables to autonomous switches.