Changes in version 2.0.0 (2026-03-08)                  

Breaking changes

  - Softplus reparameterization replaces hard-clamp positivity
    enforcement. Parameters in parts$positive (formerly parts$T and
    parts$P) are now handled internally via theta_i = softplus(z_i),
    guaranteeing strict positivity analytically. theta0[positive] must
    now be strictly > 0; v1 silently clamped zero or negative starting
    values, v2 raises an error.

  - parts API simplified. The preferred form is now list(positive = ...,
    euclidean = ...). The three-way {T, P, E} form from v1 remains
    accepted as a backwards-compatibility alias (T and P indices both
    map to positive; E maps to euclidean).

New features

  - Relative function-stall detection (FUNC_STALL_REL). In addition to
    the existing absolute stall criterion (FUNC_STALL_ABS), convergence
    is now also declared when |ΔV| / (|V| + ε) < tol_f_rel over five
    consecutive iterations. New control parameter tol_f_rel (default
    1e-9).

  - rho-based trust-region adaptation. The trust radius now adapts using
    the ratio of actual to predicted objective reduction (rho), with
    configurable rho_shrink (default 0.25), rho_grow (default 0.75),
    shrink_factor (default 0.5), and grow_factor (default 1.5). The v1
    binary accept/reject heuristic is used as a fallback when rho is not
    finite.

  - Richer per-iteration history. fit$history now contains ten columns
    (was eight): iter, f, g_inf, step_norm, df, eta, method,
    armijo_iters (new), pred_red (new), rho (new). The Lyapunov-specific
    columns (delta_V_rel, lyapunov_ok) from v1 are removed.

  - galahad_numgrad() — exported finite-difference gradient helper for
    use when analytical gradients are unavailable.

  - galahad_parts() — exported constructor that validates and assembles
    the parts argument with an optional total-parameter-count check.

  - rho_accept, use_rho_accept control options enable an alternative
    acceptance mode based on trust-region quality ratio instead of
    Armijo decrease.

Changes to defaults

| Parameter | v1 default | v2 default | Notes |
|-------------|------------
|------------|-------------------------------------| | delta_min | 0.1
| 1e-6 | Allows finer convergence near zero | | delta_max | 100.0 | 1e3
| Slightly wider upper bound | | tol_f_rel | (absent) | 1e-9 | New
relative stall criterion | | armijo_max| 20 (hard) | 20 (in control) |
Now user-configurable |

Removed features

  - Halpern averaging removed. The per-iteration Halpern candidate
    alpha_k * theta0 + (1 - alpha_k) * theta_proj is no longer
    evaluated. This saves approximately one function evaluation per
    iteration; the function-stall detection provides equivalent
    convergence assurance on the smooth objectives for which GALAHAD is
    designed.

  - Lyapunov violation tracking removed (lyapunov_violations,
    monotone_descent from diagnostics). The rho history in
    fit$history$rho provides equivalent information about step quality.

  - make_safe_function() wrappers removed. Errors in V or gradV now
    surface immediately via galahad_assert_finite(). Callers are
    expected to supply well-defined objectives; the previous
    silent-fallback behaviour could mask model specification bugs.

  - grad_clip_norm control option removed.

  - eps_safe control option removed (no longer applicable with
    softplus).

Bug fixes

  - Fixed a spurious st$z bare expression in the internal state-update
    helper that had no effect but generated lint warnings.

Citation corrections

  - Polyak (1969) corrected. Version 1 incorrectly cited the paper
    "Minimization of unsmooth functionals", Vol. 9(3), pp. 14--29. The
    correct paper implemented in GALAHAD is "The conjugate gradient
    method in extremal problems", Vol. 9(4), pp. 94--112.
    doi:10.1016/0041-5553(69)90035-4

  - Xu and An (2024) added. Citation for the regularized
    Barzilai-Borwein trust-region extension was absent from v1 despite
    the method being used. doi:10.48550/arXiv.2409.14383

  - Dugas et al. (2009) added. Citation for the softplus activation
    function was absent from v1 despite softplus being central to the
    positivity geometry. Journal of Machine Learning
    Research, 10(42), 1239--1262.
    https://www.jmlr.org/papers/v10/dugas09a.html

Development note

  - Development used AI coding assistance under continuous human
    direction; see package DESCRIPTION for full disclosure.

                 Changes in version 1.0.0 (2025-11-07)                  

  - Initial CRAN release.
  - Geometry-adaptive optimization with Lyapunov stability guarantees.
  - Three-partition geometry: log-scaled (T), positive-only (P),
    Euclidean (E).
  - Trust-region projection, Halpern averaging, Polyak / BB step-size
    control.
  - Developed at MNPRO, University of Minnesota.