AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
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numerics is a C++20 library for scientific computing.
It provides dense and sparse linear algebra, direct factorizations, Krylov solvers, ODE integrators, spectral transforms, graph algorithms, and quadrature.
The compute kernels are efficient, allocate nothing, and have no dependencies.
The layers above them state the mathematical preconditions of each algorithm, such as symmetry or positive-definiteness, as C++20 concepts.
A precondition that cannot be established at compile time is asserted by the caller and verified at run time under the diagnostic presets.查看完整 README ↗numerics is a C++20 library of numerical methods. It covers dense, banded and sparse linear
algebra, Krylov solvers, eigenvalues and the SVD, ODE integrators, PDE operators, spectral
transforms, quadrature, root finding, graphs and sampling. The arithmetic lives in num::kernel,
a set of allocation-free loops over raw pointers with no dependencies. BLAS, LAPACK, OpenMP,
CUDA, FFTW and SuiteSparse are used when they are present.
I started numerics to pull the code from my research and my numerical methods courses
(fluid simulation, stochastic modeling, graph algorithms, linear algebra) into one library. I
maintain it myself, and it keeps growing alongside my research, so use it with appropriate
caution!!
Every feature is explained by a complete program in examples/, with the output it
prints. The documentation indexes them by feature
and has a reference page for every container, concept and kernel.
include(FetchContent)
FetchContent_Declare(numerics
GIT_REPOSITORY https://github.com/AdityaDendukuri/numerics.git
GIT_TAG v1.0.0)
FetchContent_MakeAvailable(numerics)
target_link_libraries(my_program PRIVATE numerics::numerics)
numerics::core has no external dependency, and numerics::kernel is num::kernel alone.
cmake --preset dev && cmake --build --preset dev && ctest --preset dev
| Feature | Example |
|---|---|
| Refactoring after a local change, with Woodbury-corrected LU while few states differ and block LU that keeps every block before the first change | 16_factor_reuse.cpp |
| Solving with A + PQT from a factorization of A | 17_woodbury_low_rank_update.cpp |
| diag(A-1) of a sparse M-matrix from a block of random probes | 19_probed_inverse_diagonal.cpp |
| Shifted systems (sI - A)x = b at many shifts from one Hessenberg reduction, and Talbot inversion | 03_resolvent_and_expv.cpp, 13_talbot_spectral_validation.cpp |
MIT. See LICENSE and THIRD_PARTY_LICENSES.md.