Discussion Overview
The discussion revolves around solving a generalized symmetric eigenproblem using LAPACK, specifically the DSYGVD function. Participants explore issues related to stack overflow errors when handling large matrices (N > 500) and the implications of matrix conditioning on the eigenproblem's solvability.
Discussion Character
- Technical explanation
- Debate/contested
- Mathematical reasoning
Main Points Raised
- One participant reports a stack overflow when attempting to solve the eigenproblem for large N, suggesting that the array size defined in the program may be too large.
- Another participant proposes checking the stack size limit on the Linux system using the command 'ulimit -s' and suggests that compiler optimizations may be contributing to the issue.
- There is a suggestion to test the eigenproblem with either matrix A or B set to the identity matrix to determine if the problem is related to matrix conditioning.
- One participant mentions successfully using allocatable arrays to avoid stack overflow but still encounters a non-zero INFO return value from DSYGVD, indicating potential issues with matrix B being non-positive definite.
- Another participant discusses the possibility of different compiler versions affecting the results and mentions encountering an INFO value of 586, which suggests issues with the leading minor of matrix B.
- There is an ongoing exploration of whether the stack overflow and the non-zero INFO return are related or separate issues.
Areas of Agreement / Disagreement
Participants express differing views on the causes of the stack overflow and the non-zero INFO return from DSYGVD. There is no consensus on the underlying issues, and multiple competing hypotheses are presented regarding matrix conditioning and compiler behavior.
Contextual Notes
Participants note that the behavior of LAPACK functions may vary based on the specific versions of compilers and LAPACK being used, and there are unresolved questions regarding the conditioning of the matrices involved in the eigenproblem.
Who May Find This Useful
Researchers and practitioners working with large-scale eigenvalue problems in numerical linear algebra, particularly those using LAPACK and Fortran, may find this discussion relevant.