example.cpp

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#include <batmat/linalg/copy.hpp>
#include <batmat/linalg/gemm.hpp>
#include <batmat/linalg/potrf.hpp>
#include <batmat/matrix/matrix.hpp>
#include <guanaqo/print.hpp>
#include <algorithm>
#include <cmath>
#include <iostream>
#include <limits>
#include <random>
 
using batmat::index_t;
using batmat::real_t;
namespace la = batmat::linalg;
 
int main() {
    using batch_size             = std::integral_constant<index_t, 4>;
    constexpr auto storage_order = batmat::matrix::StorageOrder::ColMajor;
    // Class representing a batch of four matrices.
    using Mat = batmat::matrix::Matrix<real_t, index_t, batch_size, batch_size, storage_order>;
    // Allocate some batches of matrices (initialized to zero).
    index_t n = 3, m = n + 5;
    Mat C{{.rows = n, .cols = n}}, A{{.rows = n, .cols = m}};
    // Fill A with random values.
    std::mt19937 rng{12345};
    std::uniform_real_distribution<real_t> uni{-1.0, 1.0};
    std::ranges::generate(A, [&] { return uni(rng); });
    // Compute C = AAᵀ to make it symmetric positive definite (lower triangular part only).
    la::syrk(A, la::tril(C));
    // Allocate L for the Cholesky factors.
    Mat L{{.rows = n, .cols = n}, batmat::matrix::uninitialized};
    // Compute the Cholesky factors L of C (lower triangular).
    la::fill(0, la::triu(L));
    la::potrf(la::tril(C), la::tril(L));
    // Print the results.
    for (index_t l = 0; l < C.depth(); ++l) {
        guanaqo::print_python(std::cout << "C[" << l << "] =\n", C(l));
        guanaqo::print_python(std::cout << "L[" << l << "] =\n", L(l));
    }
    // Compute LLᵀ (in-place).
    la::syrk(la::tril(L));
    // Check that LLᵀ == C.
    int errors     = 0;
    const auto eps = std::numeric_limits<real_t>::epsilon();
    for (index_t l = 0; l < C.depth(); ++l)
        for (index_t c = 0; c < C.cols(); ++c)
            for (index_t r = c; r < C.rows(); ++r)
                errors += std::abs(C(l, r, c) - L(l, r, c)) < 10 * eps ? 0 : 1;
    return errors;
}