fullseye

iterate_fixed_point — CONVERGE iterate op

• データ種: なし → table(引数だけで決まる op —— 画像やデータの入力を取らない)
• 呼び出し: import fullseye as fs; fs.ledger.iterate_fixed_point(g='cos', x0=None, method='anderson', tol=1e-10, rtol=0.0, max_iter=500, m=5, on_fail='raise', keep_iterates=False) -> 'dict' (実装を直接呼ぶなら import converge; converge.iterate_fixed_point(g='cos', x0=None, method='anderson', tol=1e-10, rtol=0.0, max_iter=500, m=5, on_fail='raise', keep_iterates=False) -> 'dict'、台帳から引くなら opsconverge.get("iterate_fixed_point"))

使い方

Solve x = g(x) by fixed-point iteration (plain / Aitken / Anderson) and refuse if it does not converge.

g is a callable mapping an iterate (scalar or array) to one of the same shape, or the name of a built-in example ("cos": x = cos x, x0 = 1, fixed point 0.7390851332…). The residual is f_k = g(x_k) - x_k and the run has converged when ||f_k|| <= tol + rtol * ||x_k|| (2-norm). The value returned is g(x_k).

method:

• "plain" x_{k+1} = g(x_k) — the bounce-by-bounce Neumann sum for a linear map.
• "aitken" Aitken’s delta-squared on x, g(x), g(g(x)) per step (Steffensen form), component-wise; a component whose second difference vanishes takes g(g(x)).
• "anderson" Anderson acceleration (Walker & Ni 2011, Algorithm AA) with memory m, written as their eq. (3.1): gamma = argmin ||f_k - dF_k gamma||2 with dF_k = (f{k-m_k+1} - f_{k-m_k}, …, f_k - f_{k-1}), then x_{k+1} = g(x_k) - dG_k gamma (dG holds the same differences of g). The least-squares problem is solved by a QR factorisation; while cond(R) > ANDERSON_COND_MAX (1e8) the left-most (oldest) column is dropped (section 4 of Walker & Ni, after Yang et al.). On a linear map with no columns dropped this reproduces GMRES (their Theorem 2.2): the gates check it.

Returned (a table dict): x (g(x_k)), converged, n_iter, n_evals (calls of g), residual_history (||f_k|| per iterate), lipschitz_estimate (largest secant ratio of g over the stored iterates), error_estimate = L/(1-L) * ||f_k|| — the a-posteriori contraction bound on |g(x_k) - x*|, None when the estimated L is not below 1 — columns_dropped (Anderson conditioning) and method.

Refusal: divergence (non-finite residual or growth by DIVERGENCE_FACTOR = 1e8), a period-2 oscillation, stagnation or running out of max_iter raises :class:NotConvergedError (a MetricContractError) with the record attached. With on_fail="record" the record is returned instead, with x = None and the last iterate under last_iterate — never under x.

詳しい使い方ガイド

• convergent_computation ファミリ ガイド

参考(サンプルデータ・文献)

• サンプルデータ カタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。

実行できる例(この op を実際に呼ぶ検証済みサンプル)

• converge_tour — py -3.11 examples/converge_tour.py

型が繋がる次の op(table を入力に取れる)

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同カテゴリ(iterate)

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Provenance: converge.py — CONVERGE operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.

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