sparse.hpp

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/// @file sparse.hpp
/// @brief Compressed Sparse Row (CSR) matrix and operations
/// @todo Add transpose, symmetry diagnostics, and efficient diagonal extraction
/// utilities for sparse solvers and preconditioners.
#pragma once
#include "core/types.hpp"
#include "core/vector.hpp"
#include <stdexcept>
#include <vector>
 
namespace num {
 
/// @brief Sparse matrix in Compressed Sparse Row (CSR) format
///
/// Non-zero values for row i are stored in vals_[row_ptr_[i] .. row_ptr_[i+1]).
/// Corresponding column indices are in col_idx_[row_ptr_[i] .. row_ptr_[i+1]).
class SparseMatrix {
public:
  /// @brief Construct from raw CSR arrays (takes ownership)
  SparseMatrix(idx n_rows,
               idx n_cols,
               std::vector<real> vals,
               std::vector<idx> col_idx,
               std::vector<idx> row_ptr);
 
  /// @brief Build from coordinate (COO / triplet) lists
  ///
  /// Duplicate (row, col) entries are summed. Entries need not be sorted.
  static SparseMatrix from_triplets(idx n_rows,
                                    idx n_cols,
                                    const std::vector<idx>& rows,
                                    const std::vector<idx>& cols,
                                    const std::vector<real>& vals);
 
  idx n_rows() const { return n_rows_; }
  idx n_cols() const { return n_cols_; }
  idx nnz() const { return vals_.size(); }
 
  /// @brief Element access A(i,j); returns 0 if outside stored pattern  --
  /// O(nnz/n)
  real operator()(idx i, idx j) const;
 
  [[nodiscard]] const real* values() const { return vals_.data(); }
  [[nodiscard]] const idx* col_idx() const { return col_idx_.data(); }
  [[nodiscard]] const idx* row_ptr() const { return row_ptr_.data(); }
 
private:
  idx n_rows_ = 0, n_cols_ = 0;
  std::vector<real> vals_;
  std::vector<idx> col_idx_;
  std::vector<idx> row_ptr_; // size n_rows_ + 1
};
 
/// @brief y = A * x
void sparse_matvec(const SparseMatrix& A, const Vector& x, Vector& y);
 
} // namespace num