https://github.com/cometscome/zpares.jl

https://github.com/cometscome/zpares.jl

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• Host: GitHub
• URL: https://github.com/cometscome/zpares.jl
• Owner: cometscome
• Created: 2020-05-19T09:11:37.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-01-19T06:28:32.000Z (almost 3 years ago)
• Last Synced: 2024-10-13T19:39:24.588Z (2 months ago)
• Language: Julia
• Size: 594 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        

# ZPares

[![CI](https://github.com/cometscome/ZPares.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/cometscome/ZPares.jl/actions/workflows/CI.yml)

Julia wrapper for [z-Pares](https://zpares.cs.tsukuba.ac.jp).

# What is this?

We can get eigenvalues with a given domain Gamma. Now Gamma is a elipsoidal shape. 

```left``` means the left side and ```right``` means the right side. 

If you use ```ishermitian = true```, left is the minimum eigenvalue in the domain Gamma and right is the maximum in this. 

Julia 1.6 or higher is needed. 

```

z-Pares: Parallel Eigenvalue Solver

z-Pares is a package for solving generalized eigenvalue problems. z-Pares is designed to compute a few eigenvalues and eigenvectors of sparse matrices. The symmetries and definitenesses of the matrices can be exploited suitably. z-Pares implements a complex moment based contour integral eigensolver. z-Pares computes eigenvalues inside a user-specified contour path and corresponding eigenvectors. The most important feature of z-Pares is two-level Message Passing Interface (MPI) distributed parallelism.

```

# How to install

```

add https://github.com/cometscome/ZPares.jl

```

# Sample

```julia

using ZPares

using Test

using SparseArrays

function test()

    mat_size = 500

    A = zeros(ComplexF64,mat_size,mat_size)

    B = zeros(ComplexF64,mat_size,mat_size)

    for i=1:mat_size

        for j=1:mat_size

            if i==j

                A[i,j] = 2

            elseif abs(i-j) == 1

                A[i,j] = 1

            end 

        end

    end

    for i=1:mat_size

        B[i,i] = 1

    end

#    @time ZPares.eigsolve(A,B)

    left = 3.5

    right = 3.6

    ρ = (right-left)/2

    γ = (right+left)/2

    L=8

    N=32

    M=16

    Lmax=32

    γ = 0

    ρ = 0.2

    left = γ-ρ

    right = γ+ρ

    @time eigval,X,num_ev,res = ZPares.eigensolve(sparse(A),left,right)

    println("index:   eigenvalues : residuals")

    for i=1:num_ev

        println(i,"\t",eigval[i],"\t",res[i])

    end

    @time eigval,X,num_ev,res = ZPares.eigensolve(sparse(A),left,right,ishermitian=true)

    println("index:   eigenvalues : residuals")

    for i=1:num_ev

        println(i,"\t",eigval[i],"\t",res[i])

    end

    #exit()

   

    @time eigval,X,num_ev,res = ZPares.eigensolve(sparse(A),sparse(B),left,right)

    for i=1:num_ev

        println(i,"\t",eigval[i],"\t",res[i])

    end    

    @time eigval,X,num_ev,res = ZPares.eigensolve(sparse(A),sparse(B),left,right,ishermitian=true)

    for i=1:num_ev

        println(i,"\t",eigval[i],"\t",res[i])

    end 

    #e,v = eigen(A)

    #println(e)

    

end

test()

```