A Local Minimax Method Using the Generalized Nehari Manifold for Finding Differential Saddles

In order to find the first few unconstrained saddles of functionals with different types of variational structures, a new local minimax method (LMM), based on a dynamics of points on virtual geometric objects such as curves, surfaces, etc., is developed. Algorithm stability and convergence are mathematically verified. The new algorithm is tested on several benchmark examples commonly used in the literature to show its stability and efficiency, then it is applied to numerically compute saddles of a semilinear elliptic PDE of both M-type (focusing) and W-type (defocusing). The Newton’s method will also be investigated and used to accelerate the local convergence and increase the accuracy. The Nehari manifold is used in the algorithm to satisfy a crucial condition for convergence. The numerical computation is also accelerated and a comparison of computation speed between using the Nehari manifold and quadratic geometric objects on the same semilinear elliptic PDEs is given, then a mixed M and W type case is solved by the LMM with the Nehari manifold. To solve the indefinite M-type problems, the generalized Nehari manifold is introduced in detail, and a generalized dynamic system of points on it is given. The corresponding LMM with a correction technique is also justified and a convergence analysis is presented, then it is tested on an indefinite M-type case. A numerical investigation of bifurcation for an indefinite problem will be given to provide numerical evidence for PDE analysts for future stud

A Local Minimax Method Using the Generalized Nehari Manifold for Finding Differential Saddles

In order to find the first few unconstrained saddles of functionals with different types of variational structures, a new local minimax method (LMM), based on a dynamics of points on virtual geometric objects such as curves, surfaces, etc., is developed. Algorithm stability and convergence are mathematically verified. The new algorithm is tested on several benchmark examples commonly used in the literature to show its stability and efficiency, then it is applied to numerically compute saddles of a semilinear elliptic PDE of both M-type (focusing) and W-type (defocusing). The Newton’s method will also be investigated and used to accelerate the local convergence and increase the accuracy. The Nehari manifold is used in the algorithm to satisfy a crucial condition for convergence. The numerical computation is also accelerated and a comparison of computation speed between using the Nehari manifold and quadratic geometric objects on the same semilinear elliptic PDEs is given, then a mixed M and W type case is solved by the LMM with the Nehari manifold. To solve the indefinite M-type problems, the generalized Nehari manifold is introduced in detail, and a generalized dynamic system of points on it is given. The corresponding LMM with a correction technique is also justified and a convergence analysis is presented, then it is tested on an indefinite M-type case. A numerical investigation of bifurcation for an indefinite problem will be given to provide numerical evidence for PDE analysts for future stud

Adaptive local minimax Galerkin methods for variational problems

In many applications of practical interest, solutions of partial differential equation models arise as critical points of an underlying (energy) functional. If such solutions are saddle points, rather than being maxima or minima, then the theoretical framework is non-standard, and the development of suitable numerical approximation procedures turns out to be highly challenging. In this paper, our aim is to present an iterative discretization methodology for the numerical solution of nonlinear variational problems with multiple (saddle point) solutions. In contrast to traditional numerical approximation schemes, which typically fail in such situations, the key idea of the current work is to employ a simultaneous interplay of a previously developed local minimax approach and adaptive Galerkin discretizations. We thereby derive an adaptive local minimax Galerkin (LMMG) method, which combines the search for saddle point solutions and their approximation in finite-dimensional spaces in a highly effective way. Under certain assumptions, we will prove that the generated sequence of approximate solutions converges to the solution set of the variational problem. This general framework will be applied to the specific context of finite element discretizations of (singularly perturbed) semilinear elliptic boundary value problems, and a series of numerical experiments will be presented

Finding Multiple Saddle Points for G-differential Functionals and Defocused Nonlinear Problems

We study computational theory and numerical methods for finding multiple unstable solutions (saddle points) for two types of nonlinear variational functionals. The first type consists of Gateaux differentiable (G-differentiable) M-type (focused) problems. Motivated by quasilinear elliptic problems from physical applications, where energy functionals are at most lower semi-continuous with blow-up singularities in the whole space and G-differntiable in a subspace, and mathematical results and numerical methods for C1 or nonsmooth/Lipschitz saddle points existing in the literature are not applicable, we establish a new mathematical frame-work for a local minimax method and its numerical implementation for finding multiple G-saddle points with a new strong-weak topology approach. Numerical implementation in a weak form of the algorithm is presented. Numerical examples are carried out to illustrate the method. The second type consists of C^1 W-type (defocused) problems. In many applications, finding saddles for W-type functionals is desirable, but no mathematically validated numerical method for finding multiple solutions exists in literature so far. In this dissertation, a new mathematical numerical method called a local minmaxmin method (LMMM) is proposed and numerical examples are carried out to illustrate the efficiency of this method. We also establish computational theory and present the convergence results of LMMM under much weaker conditions. Furthermore, we study this algorithm in depth for a typical W-type problem and analyze the instability performances of saddles by LMMM as well