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/// Augmented-Lagrangian method for linear equality constraints `A x = b`
/// (penalty + multiplier updates over any unconstrained inner solver;
/// tolerates infeasible starts).
/// Log-barrier method for linear inequality constraints `A x ≤ b`
/// (`constrOptim`-style layer over any unconstrained inner solver).
/// Box-constrained CMA-ES with adaptive quadratic boundary penalty
/// (Hansen `BoundPenalty`, the default in pycma).
/// Memetic [`BoundedCmaEs`] with
/// Hansen-2011 injection — sibling of [`cma_inject`] over the bounded
/// outer. Inners: Nelder-Mead, Levenberg-Marquardt, L-BFGS-B.
/// Brent's method (1D root / minimum bracketing).
/// Hansen 2016 (µ/µ_W, λ)-CMA-ES with negative weights.
/// Memetic CMA-ES with Hansen-2011 injection. Inners: Nelder-Mead,
/// Levenberg-Marquardt. For L-BFGS-B inner with consistent bound
/// handling, see [`bounded_cma_inject`].
/// Differential Evolution (DE/rand/1/bin) — Storn-Price 1997 global
/// optimizer on a feasible box, fully backend-generic.
/// Pure Gauss-Newton solver for nonlinear least squares.
/// Steepest-descent solver with a pluggable line search and optional
/// heavy-ball momentum.
/// L-BFGS family — unconstrained `Lbfgs<Unbounded>` (two-loop
/// recursion) and box-constrained `Lbfgs<Bounded>` (faithful port of
/// Nocedal's L-BFGS-B v3.0). `Lbfgsb` is a type alias for
/// `Lbfgs<Bounded>`.
/// Levenberg-Marquardt solver for nonlinear least squares with
/// Nielsen 1999 damping update.
/// MA-LSCh-CMA — memetic algorithm with LS chains (inner: CMA-ES).
/// Nelder-Mead derivative-free simplex solver.
/// Projected gradient descent for box-constrained problems.
/// Elitist (1+λ) random search over a feasible box.
/// Steady-state real-coded GA with BLX-α + NAM + BGA + replace-worst.
/// Levenberg-Marquardt with box bounds (TRF — trust-region-reflective).
/// BFGS quasi-Newton solver (dense inverse-Hessian; `Vec<f64>`, nalgebra,
/// faer).
pub use AugmentedLagrangianMethod;
pub use BarrierMethod;
pub use Bfgs;
pub use BFGS;
pub use BoundedCmaEs;
pub use BoundedCmaInject;
pub use Brent;
pub use CmaEs;
pub use ;
pub use De;
pub use GaussNewton;
pub use GradientDescent;
pub use LevenbergMarquardt;
pub use ;
pub use ;
pub use ProjectedGradientDescent;
pub use RandomSearch;
pub use Ssga;
pub use Trf;