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Test 1 (Eigenvalue and condition number estimation Solver)

This test evaluates your ability to estimate the condition number of a symmetric positive definite (SPD) matrix using numerical methods.

Generate Input Data

Run the following command from the root of this package:

python3 data/generate_matrix.py --n 64 --kappa 1e3 --outA A.csv --outb b.csv

This will generate A.csv and b.csv in the working directory.

Hardware and Software Environment

  • CPU: Intel / AMD x86_64
  • Compiler: g++ (C++17)
  • Optimization: -O3
  • Libraries: Standard C++, LAPACK (for verification on small matrices)
  • OS: Linux

How to Run

To run the solver in the directory with eigenvalue_estimation.cpp file:

g++ -O3 eigenvalue_estimation.cpp -o eigenvalue_estimation -llapack -lblas
./eigenvalue_estimation data/A.csv data/b.csv

To run the plotting file using lambda_max.csv and lambda_min.csv log files:

python3 plot_convergence.py

About

Iterative methods (Power Method, Inverse Power Method) to estimate large/small eigenvalues of a given matrix and approximate its condition number.

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