Gas phase appearance and disappearance as a problem with complementarity constraints

The modeling of migration of hydrogen produced by the corrosion of the nuclear waste packages in an underground storage including the dissolution of hydrogen involves a set of nonlinear partial differential equations with nonlinear complementarity constraints. This article shows how to apply a modern and efficient solution strategy, the Newton-min method, to this geoscience problem and investigates its applicability and efficiency. In particular, numerical experiments show that the Newton-min method is quadratically convergent for this problem.Comment: Accepted for Publication in Mathematics and Computers in Simulation. Available online 6 August 2013, Mathematics and Computers in Simulation (2013

Study of compositional multiphase flow formulation using complementarity conditions

In this article, two formulations of multiphase compositional Darcy flows taking into account phase transitions are compared. The first formulation is the so-called natural variable formulation commonly used in reservoir simulation, the second has been introduced by Lauser et al. and uses the phase pressures, saturations and component fugacities as main unknowns. We will discuss how the Coats and the Lauser approaches can be used to solve a compositional multiphase flow problem with cubic equations of state of Peng and Robinson. Then, we will study the results of several synthetic cases that are representative of petroleum reservoir engineering problems and we will compare their numerical behavior

Une caractérisation algorithmique de la P-matricité II: ajustements, raffinements et validation

International audienceThe paper "An algorithmic characterization of P-matricity" (SIAM Journal on Matrix Analysis and Applications, 34:3 (2013) 904–916, by the same authors as here) implicitly assumes that the iterates generated by the Newton-min algorithm for solving a linear complementarity problem of dimension n, which reads 0 ⩽ x ⊥ (M x + q) ⩾ 0, are uniquely determined by some index subsets of [[1, n]]. Even if this is satisfied for a subset of vectors q that is dense in R^n, this assumption is improper, in particular in the statements where the vector q is not subject to restrictions. The goal of the present contribution is to show that, despite this blunder, the main result of that paper is preserved. This one claims that a nondegenerate matrix M is a P-matrix if and only if the Newton-min algorithm does not cycle between two distinct points, whatever is q. The proof is not more complex, requiring only some adjustments, which are essential however.L'article "An algorithmic characterization of P-matricity" (SIAM Journal on Matrix Analysis and Applications, 34:3 (2013) 904–916, par les mêmes auteurs qu'ici) suppose implicitement que les itérés générés par l'algorithme de Newton-min pour résoudre le problème de complémentarité linéaire de dimension n, qui s'écrit 0 ⩽ x ⊥ (M x + q) ⩾ 0, sont déterminés de manière unique par des sous-ensembles d'indices de [[1, n]]. Même si cette hypothèse est vérifiée pour un sous-ensemble de vecteurs q qui est dense dans R^n, elle n'est pas appropriée, en particulier dans les énoncés où le vecteur q n'est pas soumis à des restrictions. Le but du la contribution présente est de montrer que, malgré cette bévue, le résultat principal de l'article est préservé. Celui-ci affirme qu'une matrice non dégénérée M est une P-matrice si, et seulement si, l'algorithme de Newton-min ne cycle pas entre deux points distincts, quel que soit q. La démonstration n'est pas plus complexe et ne requiert que quelques ajustements, qui sont cependant essentiels

An algorithmic characterization of P-matricity

International audienceIt is shown that a matrix M is a P-matrix if and only if, whatever is the vector q, the Newton-min algorithm does not cycle between two points when it is used to solve the linear complementarity problem 0 ≤ x ⊥ (Mx+q) ≥ 0.Nous montrons dans cet article qu'une matrice M est une P-matrice si, et seulement si, quel que soit le vecteur q, l'algorithme de Newton-min ne fait pas de cycle de deux points lorsqu'il est utilisé pour résoudre le problème de compl\émentarité linéaire 0 ≤ x ⊥ (Mx+q) ≥ 0

Nonconvergence of the plain Newton-min algorithm for linear complementarity problems with a P-matrix --- The full report.

The plain Newton-min algorithm to solve the linear complementarity problem (LCP for short) 0 ≤ x ⊥ (Mx+q) ≥ 0 can be viewed as a nonsmooth Newton algorithm without globalization technique to solve the system of piecewise linear equations min(x,Mx+q)=0, which is equivalent to the LCP. When M is an M-matrix of order n, the algorithm is known to converge in at most n iterations. We show in this paper that this result no longer holds when M is a P-matrix of order ≥ 3, since then the algorithm may cycle. P-matrices are interesting since they are those ensuring the existence and uniqueness of the solution to the LCP for an arbitrary q. Incidentally, convergence occurs for a P-matrix of order 1 or 2.L'algorithme Newton-min, utilisé pour résoudre le problème de complémentarité linéaire (PCL) 0 ≤ x ⊥ (Mx+q) ≥ 0 peut être interprété comme un algorithme de Newton non lisse sans globalisation cherchant à résoudre le système d'équations linéaires par morceaux min(x,Mx+q)=0, qui est équivalent au PCL. Lorsque M est une M-matrice d'ordre n, on sait que l'algorithme converge en au plus n itérations. Nous montrons dans cet article que ce résultat ne tient plus lorsque M est une P-matrice d'ordre n ≥ 3 ; l'algorithme peut en effet cycler dans ce cas. On a toutefois la convergence de l'algorithme pour une P-matrice d'ordre 1 ou 2

A posteriori error estimates for a compositional two-phase flow with nonlinear complementarity constraints

International audienceIn this work, we develop an a-posteriori-steered algorithm for a compositional two-phase flow with exchange of components between the phases in porous media. As a model problem, we choose the two-phase liquid-gas flow with appearance and disappearance of the gas phase formulated as a system of nonlinear evolutive partial differential equations with nonlinear complementarity constraints. The discretization of our model is based on the backward Euler scheme in time and the finite volume scheme in space. The resulting nonlinear system is solved via an inexact semismooth Newton method. The key ingredient for the a posteriori analysis are the discretization, linearization, and algebraic flux reconstructions allowing to devise estimators for each error component. These enable to formulate criteria for stopping the iterative algebraic solver and the iterative linearization solver whenever the corresponding error components do not affect significantly the overall error. Numerical experiments are performed using the Newton-min algorithm as well as the Newton-Fischer-Burmeister algorithm in combination with the GMRES iterative linear solver to show the efficiency of the proposed adaptive method

Adaptive inexact smoothing Newton method for a nonconforming discretization of a variational inequality

International audienceWe develop in this work an adaptive inexact smoothing Newton method for a nonconforming discretization of a variational inequality. As a model problem, we consider the contact problem between two membranes. Discretized with the finite volume method, this leads to a nonlinear algebraic system with complementarity constraints. The non-differentiability of the arising nonlinear discrete problem a priori requests the use of an iterative linearization algorithm in the semismooth class like, e.g., the Newton-min. In this work, we rather approximate the inequality constraints by a smooth nonlinear equality, involving a positive smoothing parameter that should be drawn down to zero. This makes it possible to directly apply any standard linearization like the Newton method. The solution of the ensuing linear system is then approximated by any iterative linear algebraic solver. In our approach, we carry out an a posteriori error analysis where we introduce potential reconstructions in discrete subspaces included in H1 (Ω), as well as H (div, Ω)-conforming discrete equilibrated flux reconstructions. With these elements, we design an a posteriori estimate that provides guaranteed upper bound on the energy error between the unavailable exact solution of the continuous level and a postprocessed, discrete, and available approximation, and this at any resolution step. It also offers a separation of the different error components, namely, discretization, smoothing, linearization, and algebraic. Moreover, we propose stopping criteria and design an adaptive algorithm where all the iterative procedures (smoothing, linearization, algebraic) are adaptively stopped; this is in particular our way to fix the smoothing parameter. Finally, we numerically assess the estimate and confirm the performance of the proposed adaptive algorithm, in particular in comparison with the semismooth Newton method

A Composite Hexahedral Mixed Finite Element with Kershaw meshes

A Composite Hexahedral Mixed Finite Element for hexahedral meshes is presented. This composite element is based on the decomposition of the hexahedron into five tetrahedrons. A diffusion equation with an anisotropic diffusion coefficient and a known exact solution is solved on Kershaw meshes. Error charts for the $L^2$-error in both the scalar and vector variables are shown

Fast solution of boundary integral equations for elasticity around a crack network: a comparative study

International audienceBecause of the non-local nature of the integral kernels at play, the discretization of boundary integral equations leads to dense matrices, which would imply high computational complexity. Acceleration techniques, such as hierarchical matrix strategies combined with Adaptive Cross Approximation (ACA), are available in literature. Here we apply such a technique to the solution of an elastostatic problem, arising from industrial applications, posed at the surface of highly irregular cracks networks

Efficient approximations of the fisher matrix in neural networks using kronecker product singular value decomposition

We design four novel approximations of the Fisher Information Matrix (FIM) that plays a central role in natural gradient descent methods for neural networks. The newly proposed approximations are aimed at improving Martens and Grosse’s Kronecker-factored block diagonal (KFAC) one. They rely on a direct minimization problem, the solution of which can be computed via the Kronecker product singular value decomposition technique. Experimental results on the three standard deep auto-encoder benchmarks showed that they provide more accurate approximations to the FIM. Furthermore, they outperform KFAC and state-of-the-art first-order methods in terms of optimization speed