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Everything about Jacobi eigenvalue algorithm
Numerical linear algebra algorithm
In numerical linear algebra, the Jacobi eigenvalue algorithm is an iterative method for the calculation of the eigenvalues and eigenvectors of a real symmetric matrix. It is named after Carl Gustav Jacob Jacobi, who first proposed the method in 1846, but it only became widely used in the 1950s with the advent of computers. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Jacobi eigenvalue algorithmA 5-minute orientation
- Skim a recent paper: Computing Accurate Eigenvalues using a Mixed-Precision Jacobi AlgorithmInfluential work since 2022
- Read the landmark paper: An efficient Jacobi-like algorithm for parallel eigenvalue computationCited 70 times
- Go sideways: PreconditionerA closely related topic
- Go sideways: Arrowhead matrixA closely related topic
📖 Definitions
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🏆 Landmark papers (most cited)
An efficient Jacobi-like algorithm for parallel eigenvalue computation
Accelerating Parallel Jacobi Method for Matrix Eigenvalue Computation in DOA Estimation Algorithm
A structure-preserving Jacobi algorithm for quaternion Hermitian eigenvalue problems
A parallel additive Schwarz preconditioned Jacobi–Davidson algorithm for polynomial eigenvalue problems in quantum dot simulation
A new algorithm on the inverse eigenvalue problem for double dimensional Jacobi matrices
High-Performance Matrix Eigenvalue Decomposition Using the Parallel Jacobi Algorithm on FPGA
Parallel block Jacobi eigenvalue algorithms using systolic arrays
Super-Exponentially Convergent Parallel Algorithm for a Fractional Eigenvalue Problem of Jacobi-Type
🆕 Influential since 2022
📚 Books worth reading
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