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Convergence along complete conjugate directions

Proved
VectorSpaceOpt.conjugate_directions_converge

by wenxinzhang · Aug 25, 2026 · Mathlib c5ea003 (Lean v4.30.0)

conjugate-directionsconvergencedense-spanorthogonality

Let Q be self-adjoint and coercively bounded on a real Hilbert space, and let xStar solve Q xStar = b. Suppose p n is a sequence of nonzero, pairwise Q-orthogonal directions whose closed linear span is the whole space. Define residuals r n = b - Q (x n) and take each next iterate by exact minimization along p n, using the displayed scalar ratio in §10.6. Then

⟨rn,pk⟩=0(k<n),xn→x∗.⟨r_n,p_k⟩=0 \quad (k<n), \qquad x_n → x^*.⟨rn​,pk​⟩=0(k<n),xn​→x∗.

This is Luenberger's method-of-conjugate-directions theorem. Explicit nonzeroness prevents hidden zero denominators, while density is the completeness hypothesis that converts all finite orthogonality relations into norm convergence.

Preamble
import Definitions.Def_VectorSpaceOpt_conjugate_gradient

open Filter Set
open scoped RealInnerProductSpace
Formal statement
namespace VectorSpaceOpt

/-- Luenberger, Chapter 10, §10.6, Theorem 1. -/
theorem conjugate_directions_converge
    {H : Type*} [NormedAddCommGroup H] [InnerProductSpace ℝ H] [CompleteSpace H]
    (Q : H →L[ℝ] H) (b xStar : H) (m M : ℝ) (hm : 0 < m)
    (hself : IsRealSelfAdjoint Q) (hbounds : IsCoerciveBetween Q m M)
    (hxStar : Q xStar = b) (p x r : ℕ → H)
    (hpne : ∀ n, p n ≠ 0)
    (hconj : ∀ i j, i ≠ j → ⟪p i, Q (p j)⟫ = 0)
    (hdense : closure ((Submodule.span ℝ (Set.range p) : Submodule ℝ H) : Set H) = Set.univ)
    (hres : ∀ n, r n = b - Q (x n))
    (hstep : ∀ n,
      x (n + 1) = x n + (⟪p n, r n⟫ / ⟪p n, Q (p n)⟫) • p n) :
    (∀ n k, k < n → ⟪r n, p k⟫ = 0) ∧ Tendsto x atTop (nhds xStar) := by
  sorry

end VectorSpaceOpt
Source
David G. Luenberger, Optimization by Vector Space Methods (Wiley, 1969), Chapter 10, §10.6, Theorem 1, printed pp. 291–292 (physical PDF pp. 309–310). Scan: https://sites.science.oregonstate.edu/~show/old/142_Luenberger.pdf
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What the Lean code literally says, in plain math · gpt-5

Let HHH be a complete real inner-product space, let Q:H→HQ:H→HQ:H→H be continuous and real-linear, and let b,x∗∈Hb,x^*∈Hb,x∗∈H and m,M∈Rm,M∈ℝm,M∈R. Assume m>0m>0m>0, QQQ is self-adjoint, m‖z‖2≤⟨z,Qz⟩≤M‖z‖2m‖z‖²≤⟨z,Qz⟩≤M‖z‖²m‖z‖2≤⟨z,Qz⟩≤M‖z‖2 for every zzz, and Qx∗=bQx^*=bQx∗=b. Let p,x,r:N→Hp,x,r:ℕ→Hp,x,r:N→H be arbitrary sequences satisfying: pn≠0p_n≠0pn​=0 for every nnn; ⟨pi,Qpj⟩=0⟨p_i,Qp_j⟩=0⟨pi​,Qpj​⟩=0 for every distinct pair i,ji,ji,j; the closure of the real linear span of the range of ppp is all of HHH; rn=b−Qxnr_n=b-Qx_nrn​=b−Qxn​ for every nnn; and xn+1=xn+(⟨pn,rn⟩/⟨pn,Qpn⟩)pnx_{n+1}=x_n+(⟨p_n,r_n⟩/⟨p_n,Qp_n⟩)p_nxn+1​=xn​+(⟨pn​,rn​⟩/⟨pn​,Qpn​⟩)pn​ for every nnn. Then both of the following hold: for all naturals n,kn,kn,k with k<nk<nk<n, ⟨rn,pk⟩=0⟨r_n,p_k⟩=0⟨rn​,pk​⟩=0; and xnx_nxn​ tends to x∗x^*x∗ as n→∞n→∞n→∞ in the norm topology. The update's division is total, but m>0m>0m>0 and pn≠0p_n≠0pn​=0 imply its denominator ⟨pn,Qpn⟩⟨p_n,Qp_n⟩⟨pn​,Qpn​⟩ is positive. No recurrence for the directions pnp_npn​ or residuals beyond the displayed assumptions is imposed.

Human review
  • Endorsed by Shuze Chen · Aug 26, 2026

  • Endorsed by wenxinzhang · Aug 26, 2026

    Confirmed by the mission captain (proposal self-audit).

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