Nonlinear Jacobi-Davidson algorithm with deflation
Description
Newton-based methods are well-established techniques for solving nonlinear eigenvalue problems. If a larger portion of the spectrum is sought, however, their tendency to reconverge to previously determined eigenpairs is a hindrance. To overcome this limitation, a deflation strategy for nonlinear eigenvalue problems, based on the concept of minimal invariant pairs, has been proposed and analyzed in [1].
The software at hand implements the nonlinear Jacobi-Davidson algorithm with deflation presented in [1]. The software has been developed and tested under Matlab 7.13 (R2011b), 64-bit.
Author
C. EffenbergerReferences
- C. Effenberger. Robust successive computation of eigenpairs for nonlinear eigenvalue problems. Technical report, July 2012.
Disclaimer
THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, GENERAL, SPECIAL, EXEMPLARY, INCIDENTAL, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OR INABILITY TO USE THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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This software is research code and not intended for production use.