/**
 * Optimal SOR relaxation factor for the 7-point Laplacian on a cell-centered grid
 * with Neumann boundaries (the discretisation {@link v3_grid_solve_pressure} uses).
 *
 * For a Poisson problem on a uniform grid of side `N`, the Jacobi iteration matrix
 * has spectral radius `ρ_J ≈ cos(π/N)`. The classical optimal SOR factor is
 *
 *     ω_opt = 2 / (1 + √(1 - ρ_J²)) = 2 / (1 + sin(π/N))
 *
 * On non-cubic grids the slowest-converging mode has a wavelength bounded below by
 * the longest side, so we use `N = max(res_x, res_y, res_z)`. This is slightly
 * conservative on lopsided grids — the true optimum would also account for the
 * shorter axes — but it never overshoots into the divergent ω ≥ 2 region, and the
 * dropoff in convergence rate around the optimum is mild.
 *
 * Reference values:
 *   N =   8 → ω ≈ 1.446
 *   N =  16 → ω ≈ 1.674
 *   N =  32 → ω ≈ 1.821
 *   N =  64 → ω ≈ 1.906
 *   N = 128 → ω ≈ 1.952
 *
 * Note the value is always in `(1, 2)` — `1 < ω` for `N ≥ 2`, and `ω → 2` as
 * `N → ∞`. A hardcoded 1.7 is roughly correct for 16³ and dramatically too
 * cautious for 128³.
 *
 * @param {number} res_x
 * @param {number} res_y
 * @param {number} res_z
 * @returns {number} ω in (0, 2)
 */
export function optimal_sor_omega(res_x: number, res_y: number, res_z: number): number;
//# sourceMappingURL=optimal_sor_omega.d.ts.map