The aim of this study is to create a fast and stable iterative technique for numerical solution of a quasi-linear elliptic pressure equation. We developed a modified version of the Anderson acceleration(AA)algorithm t...The aim of this study is to create a fast and stable iterative technique for numerical solution of a quasi-linear elliptic pressure equation. We developed a modified version of the Anderson acceleration(AA)algorithm to fixed-point(FP) iteration method. It computes the approximation to the solutions at each iteration based on the history of vectors in extended space, which includes the vector of unknowns, the discrete form of the operator, and the equation's right-hand side. Several constraints are applied to AA algorithm, including a limitation of the time step variation during the iteration process, which allows switching to the base FP iterations to maintain convergence. Compared to the base FP algorithm, the improved version of the AA algorithm enables a reliable and rapid convergence of the iterative solution for the quasi-linear elliptic pressure equation describing the flow of particle-laden yield-stress fluids in a narrow channel during hydraulic fracturing, a key technology for stimulating hydrocarbon-bearing reservoirs. In particular, the proposed AA algorithm allows for faster computations and resolution of unyielding zones in hydraulic fractures that cannot be calculated using the FP algorithm. The quasi-linear elliptic pressure equation under consideration describes various physical processes, such as the displacement of fluids with viscoplastic rheology in a narrow cylindrical annulus during well cementing,the displacement of cross-linked gel in a proppant pack filling hydraulic fractures during the early stage of well production(fracture flowback), and multiphase filtration in a rock formation. We estimate computational complexity of the developed algorithm as compared to Jacobian-based algorithms and show that the performance of the former one is higher in modelling of flows of viscoplastic fluids. We believe that the developed algorithm is a useful numerical tool that can be implemented in commercial simulators to obtain fast and converged solutions to the non-linear problems described above.展开更多
Anderson acceleration(AA)is an extrapolation technique designed to speed up fixed-point iterations.For optimization problems,we propose a novel algorithm by combining the AA with the energy adaptive gradient method(AE...Anderson acceleration(AA)is an extrapolation technique designed to speed up fixed-point iterations.For optimization problems,we propose a novel algorithm by combining the AA with the energy adaptive gradient method(AEGD)[arXiv:2010.05109].The feasibility of our algorithm is ensured in light of the convergence theory for AEGD,though it is not a fixed-point iteration.We provide rigorous convergence rates of AA for gradient descent(GD)by an acceleration factor of the gain at each implementation of AA-GD.Our experimental results show that the proposed AA-AEGD algorithm requires little tuning of hyperparameters and exhibits superior fast convergence.展开更多
Anderson acceleration is a kind of effective method for improving the convergence of the general fixed point iteration.In the linear case,Anderson acceleration can be used to improve the convergence rate of matrix spl...Anderson acceleration is a kind of effective method for improving the convergence of the general fixed point iteration.In the linear case,Anderson acceleration can be used to improve the convergence rate of matrix splitting based iterative methods.In this paper,by using Anderson acceleration on general splitting iterative methods for linear systems,three classes of methods are given.The first one is obtained by directly applying Anderson acceleration on splitting iterative methods.For the second class of methods,Anderson acceleration is used periodically in the splitting iteration process.The third one is constructed by combining the Anderson acceleration and split iteration method in each iteration process.The key of this class of method is to determine a combination coefficient for Anderson acceleration and split iteration method.One optimal combination coefficient is given.Some theoretical results about the convergence of the considered three methods are established.Numerical experiments show that the proposed methods are effective.展开更多
The solution of minimum-time feedback optimal control problems is generally achieved using the dynamic programming approach,in which the value function must be computed on numerical grids with a very large number of p...The solution of minimum-time feedback optimal control problems is generally achieved using the dynamic programming approach,in which the value function must be computed on numerical grids with a very large number of points.Classical numerical strategies,such as value iteration(VI)or policy iteration(PI)methods,become very inefficient if the number of grid points is large.This is a strong limitation to their use in real-world applications.To address this problem,the authors present a novel multilevel framework,where classical VI and PI are embedded in a full-approximation storage(FAS)scheme.In fact,the authors will show that VI and PI have excellent smoothing properties,a fact that makes them very suitable for use in multilevel frameworks.Moreover,a new smoother is developed by accelerating VI using Anderson’s extrapolation technique.The effectiveness of our new scheme is demonstrated by several numerical experiments.展开更多
基金partial financial support from Gazpromneft Science and Technology Center。
文摘The aim of this study is to create a fast and stable iterative technique for numerical solution of a quasi-linear elliptic pressure equation. We developed a modified version of the Anderson acceleration(AA)algorithm to fixed-point(FP) iteration method. It computes the approximation to the solutions at each iteration based on the history of vectors in extended space, which includes the vector of unknowns, the discrete form of the operator, and the equation's right-hand side. Several constraints are applied to AA algorithm, including a limitation of the time step variation during the iteration process, which allows switching to the base FP iterations to maintain convergence. Compared to the base FP algorithm, the improved version of the AA algorithm enables a reliable and rapid convergence of the iterative solution for the quasi-linear elliptic pressure equation describing the flow of particle-laden yield-stress fluids in a narrow channel during hydraulic fracturing, a key technology for stimulating hydrocarbon-bearing reservoirs. In particular, the proposed AA algorithm allows for faster computations and resolution of unyielding zones in hydraulic fractures that cannot be calculated using the FP algorithm. The quasi-linear elliptic pressure equation under consideration describes various physical processes, such as the displacement of fluids with viscoplastic rheology in a narrow cylindrical annulus during well cementing,the displacement of cross-linked gel in a proppant pack filling hydraulic fractures during the early stage of well production(fracture flowback), and multiphase filtration in a rock formation. We estimate computational complexity of the developed algorithm as compared to Jacobian-based algorithms and show that the performance of the former one is higher in modelling of flows of viscoplastic fluids. We believe that the developed algorithm is a useful numerical tool that can be implemented in commercial simulators to obtain fast and converged solutions to the non-linear problems described above.
基金partially supported by the National Science Foundation under(Grant DMS No.1812666)。
文摘Anderson acceleration(AA)is an extrapolation technique designed to speed up fixed-point iterations.For optimization problems,we propose a novel algorithm by combining the AA with the energy adaptive gradient method(AEGD)[arXiv:2010.05109].The feasibility of our algorithm is ensured in light of the convergence theory for AEGD,though it is not a fixed-point iteration.We provide rigorous convergence rates of AA for gradient descent(GD)by an acceleration factor of the gain at each implementation of AA-GD.Our experimental results show that the proposed AA-AEGD algorithm requires little tuning of hyperparameters and exhibits superior fast convergence.
基金funded by the National Natural Science Foun China(Grant Nos.12171045,11671051).
文摘Anderson acceleration is a kind of effective method for improving the convergence of the general fixed point iteration.In the linear case,Anderson acceleration can be used to improve the convergence rate of matrix splitting based iterative methods.In this paper,by using Anderson acceleration on general splitting iterative methods for linear systems,three classes of methods are given.The first one is obtained by directly applying Anderson acceleration on splitting iterative methods.For the second class of methods,Anderson acceleration is used periodically in the splitting iteration process.The third one is constructed by combining the Anderson acceleration and split iteration method in each iteration process.The key of this class of method is to determine a combination coefficient for Anderson acceleration and split iteration method.One optimal combination coefficient is given.Some theoretical results about the convergence of the considered three methods are established.Numerical experiments show that the proposed methods are effective.
文摘The solution of minimum-time feedback optimal control problems is generally achieved using the dynamic programming approach,in which the value function must be computed on numerical grids with a very large number of points.Classical numerical strategies,such as value iteration(VI)or policy iteration(PI)methods,become very inefficient if the number of grid points is large.This is a strong limitation to their use in real-world applications.To address this problem,the authors present a novel multilevel framework,where classical VI and PI are embedded in a full-approximation storage(FAS)scheme.In fact,the authors will show that VI and PI have excellent smoothing properties,a fact that makes them very suitable for use in multilevel frameworks.Moreover,a new smoother is developed by accelerating VI using Anderson’s extrapolation technique.The effectiveness of our new scheme is demonstrated by several numerical experiments.
