4 research outputs found
Normalized Wolfe-Powell-type local minimax method for finding multiple unstable solutions of nonlinear elliptic PDEs
The local minimax method (LMM) proposed in [Y. Li and J. Zhou, SIAM J. Sci.
Comput., 23(3), 840--865 (2001)] and [Y. Li and J. Zhou, SIAM J. Sci. Comput.,
24(3), 865--885 (2002)] is an efficient method to solve nonlinear elliptic
partial differential equations (PDEs) with certain variational structures for
multiple solutions. The steepest descent direction and the Armijo-type
step-size search rules are adopted in [Y. Li and J. Zhou, SIAM J. Sci. Comput.,
24(3), 865--885 (2002)] and play a significant role in the performance and
convergence analysis of traditional LMMs. In this paper, a new algorithm
framework of the LMMs is established based on general descent directions and
two normalized (strong) Wolfe-Powell-type step-size search rules. The
corresponding algorithm framework named as the normalized Wolfe-Powell-type LMM
(NWP-LMM) is introduced with its feasibility and global convergence rigorously
justified for general descent directions. As a special case, the global
convergence of the NWP-LMM algorithm combined with the preconditioned steepest
descent (PSD) directions is also verified. Consequently, it extends the
framework of traditional LMMs. In addition, conjugate gradient-type (CG-type)
descent directions are utilized to speed up the NWP-LMM algorithm. Finally,
extensive numerical results for several semilinear elliptic PDEs are reported
to profile their multiple unstable solutions and compared for different
algorithms in the LMM's family to indicate the effectiveness and robustness of
our algorithms. In practice, the NWP-LMM combined with the CG-type direction
indeed performs much better than its known LMM companions.Comment: 27 pages, 9 figures; Accepted by SCIENCE CHINA Mathematics on January
17, 202
Nonmonotone local minimax methods for finding multiple saddle points
In this paper, by designing a normalized nonmonotone search strategy with the
Barzilai--Borwein-type step-size, a novel local minimax method (LMM), which is
a globally convergent iterative method, is proposed and analyzed to find
multiple (unstable) saddle points of nonconvex functionals in Hilbert spaces.
Compared to traditional LMMs with monotone search strategies, this approach,
which does not require strict decrease of the objective functional value at
each iterative step, is observed to converge faster with less computations.
Firstly, based on a normalized iterative scheme coupled with a local peak
selection that pulls the iterative point back onto the solution submanifold, by
generalizing the Zhang--Hager (ZH) search strategy in the optimization theory
to the LMM framework, a kind of normalized ZH-type nonmonotone step-size search
strategy is introduced, and then a novel nonmonotone LMM is constructed. Its
feasibility and global convergence results are rigorously carried out under the
relaxation of the monotonicity for the functional at the iterative sequences.
Secondly, in order to speed up the convergence of the nonmonotone LMM, a
globally convergent Barzilai--Borwein-type LMM (GBBLMM) is presented by
explicitly constructing the Barzilai--Borwein-type step-size as a trial
step-size of the normalized ZH-type nonmonotone step-size search strategy in
each iteration. Finally, the GBBLMM algorithm is implemented to find multiple
unstable solutions of two classes of semilinear elliptic boundary value
problems with variational structures: one is the semilinear elliptic equations
with the homogeneous Dirichlet boundary condition and another is the linear
elliptic equations with semilinear Neumann boundary conditions. Extensive
numerical results indicate that our approach is very effective and speeds up
the LMMs significantly.Comment: 32 pages, 7 figures; Accepted by Journal of Computational Mathematics
on January 3, 202