The one-step state update of Algorithm 2.7 on triples (x_k, y_k, α_k) with x_k ∈ Q.
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Algorithm 2.7: for a simple closed convex set Q, objective f, step parameter L,
reciprocal condition number q_f, initial feasible point x₀ ∈ Q, and admissible initial
scalar α₀ ∈ (√q_f, 2 (3 + q_f) / (3 + √(21 + 4 q_f))], the recursive state
(x_k, y_k, α_k) starts from (x₀, x₀, α₀) and applies the projected step
x_{k+1} = x_Q(y_k; L), the scalar update
α_{k+1} = constantStepSchemeIIAlphaNext q_f α_k, and the textbook type-II momentum formula
for y_{k+1}. The step parameter is stored at the owner level as the positive inverse-stepsize
datum L : NNRealˣ, matching the projected-gradient owner API. The textbook
ℝⁿ statement is the specialization E = EuclideanSpace ℝ (Fin n).
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The main iterate sequence x_k of the recursive simple-set type-II trajectory.
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The extrapolated sequence y_k of the recursive simple-set type-II trajectory.
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The scalar sequence α_k of the recursive simple-set type-II trajectory.
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The recursive simple-set type-II state satisfies the one-step state update law.
The recursive simple-set type-II iterates satisfy the textbook projected step
x_{k+1} = x_Q(y_k; L).
The recursive simple-set type-II scalar sequence uses the canonical scheme-II update
constantStepSchemeIIAlphaNext.
The recursive simple-set type-II extrapolated points satisfy the textbook momentum update.
If q_f ∈ [0, 1), then every scalar in the recursive simple-set type-II trajectory lies in
(0, 1).
The recursive Algorithm 2.7 trajectory, viewed through the owner type-II momentum recurrence API.