Alphabeta Math
TheoremStatement: AI-adaptedProof: AI-adaptedPipeline-generated
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Fraction-to-boundary steps preserve positivity and exact primal-dual feasibility certifies a gap

Statement

In symmetric-cone mode of Finite-budget primal-dual Newton methods for LP, convex QP, and symmetric cones, let s,z be strictly interior primal and dual cone variables and let αˉs,αˉz∈(0,1] be their largest boundary-preserving step fractions. If 0<βs≤αˉs, 0<βz≤αˉz, and 0<η<1, then α=ηmin⁡(βs,βz) keeps both updated cone variables interior. The conservative fractions in the algorithm's Lorentz and PSD branches satisfy these inequalities. For any exactly primal- and dual-feasible conic pair, the gap ⟨z,s⟩ is a rigorous upper bound on primal suboptimality.

In LP/QP mode, the coordinate fraction-to-boundary formula keeps x,s>0. For the convex QP of that algorithm, if x is primal feasible, s≥0, y is free, and the exact stationarity residual Qx+c−ATy−s vanishes, then 0≤f0(x)−p∗≤xTs. For LP/QP iterates with full-row-rank A, Q⪰0, and x,s>0, the exact Newton matrix is nonsingular. Thus a requested gap is certified only under exact feasibility and stationarity conditions. Nonzero residual tolerances alone are not a certificate. Every finite iteration cap returns a status after finitely many bounded updates; a failure status proves no infeasibility claim.

Facts & Assumptions

Given: The proper-cone modes, fraction formulas, exact residual audits, and Lagrangians in the algorithm and conic model.

[F1]

For conic primal and dual feasible variables, cTx−(bTy−⟨h,z⟩)=⟨z,h−Gx⟩ (Conic, LP, and convex QP primal-dual models).

[F2]

A proper cone is closed and convex, and K∗={z:⟨z,k⟩≥0 for every k∈K} (Conic, LP, and convex QP primal-dual models).

[F3]

The QP Lagrangian is L(w,y,s)=12wTQw+cTw+yT(b−Aw)−sTw (Conic, LP, and convex QP primal-dual models).

[F4]

Every primal feasible point and dual feasible pair satisfy q(y,s)≤p∗ (Every Lagrangian dual feasible pair is a lower-bound certificate).

[F5]

A differentiable convex function lies above its tangent plane (Differentiable convex functions are characterized by the gradient inequality).

[F6]

The LP/QP primal-dual Newton matrix has trivial kernel under Q⪰0, full-row-rank A, and x,s>0 (The LP/QP primal-dual Newton matrix is nonsingular from a strict positive start).

[F7]

A real symmetric matrix has an orthonormal eigenbasis (Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis), so its spectral norm is at most its Frobenius norm.

Proof

1.1F2givenalgebra

Because s is interior, a small positive segment from s in direction Δs remains in K. The set of feasible fractions in [0,1] is closed by [F2], so its maximum αˉs>0 is attained and s+αˉsΔs∈K. Any 0<α≤ηαˉs has θ=α/αˉs<1, and the update is (1−θ)s+θ(s+αˉsΔs). An open ball about s lies in K; scaling that ball by 1−θ>0 and adding θ(s+αˉsΔs)∈K shows the updated point is interior. The same argument applies to z in K∗. In the orthant, the coordinate-ratio formula is the maximal nonnegative boundary fraction. For a negative coordinate direction, α≤η(−xi/Δxi) gives xi+αΔxi≥(1−η)xi>0; nonnegative directions preserve positivity, and the same two cases apply to s.

1.2F7givenalgebra

For a Lorentz factor, write s=(t,u) and Δs=(t˙,v), with t>∥u∥2. Set q=t2−∥u∥22>0 and c=∣t˙∣+∑i∣vi∣. If c=0 the step is unchanged. Otherwise t−∥u∥2=q/(t+∥u∥2)≥q/(2t), while ∣t˙∣+∥v∥2≤c. The algorithm's safe fraction β=min⁡(1,q/(2tc)) therefore gives α=ηβ with α(∣t˙∣+∥v∥2)<t−∥u∥2. The triangle inequality then yields t+αt˙>∥u+αv∥2, so the updated Lorentz vector is interior. For a PSD factor Y≻0 and symmetric direction D, let C=L(Y−1)L(D) as in the algorithm, where L(M)=∑i,j∣Mij∣. If C=0, then D=0. Otherwise set B=Y−1/2DY−1/2. Submultiplicativity of the operator norm and [F7] give ∥B∥2≤∥Y−1∥2∥D∥2≤∥Y−1∥F∥D∥F≤C. Since α=ηmin⁡(1,1/C), αC<1, and for every nonzero w, wT(I+αB)w≥(1−α∥B∥2)∥w∥22>0. Thus Y+αD=Y1/2(I+αB)Y1/2≻0. The same bounds apply separately to primal and dual factors, so each computed safe fraction is no larger than its maximal boundary-preserving fraction.

1.3F1F2algebra

For any feasible conic pair, [F1] and [F2] give bTy−⟨h,z⟩≤cTx. Taking the infimum over primal feasible x and the supremum over dual feasible pairs shows that a feasible dual value is a lower bound on p∗. Hence a computed primal-dual feasible pair with exact gap ⟨z,s⟩≤ε is an ε-suboptimality certificate.

1.4F3F4F5givenalgebra

In LP/QP mode, exact Ax=b, x≥0, s≥0, and Qx+c−ATy−s=0 make ∇wL(x,y,s)=0. To apply [F4], use the smooth inequality convention gi(w)=−wi≤0, multiplier λ=s, and equality multiplier ν=−y. Since Q⪰0, L is convex, so [F5] implies L(w,y,s)≥L(x,y,s) for every w; therefore q(y,s)=L(x,y,s)=f0(x)−xTs. By [F4], q(y,s)≤p∗, and primal feasibility gives p∗≤f0(x). Thus 0≤f0(x)−p∗≤xTs.

1.5F6given

In LP/QP mode, [F6] supplies a unique exact Newton direction under the stated rank and strict-interiority hypotheses. Symmetric-cone mode may have a singular derivative, which the algorithm tests and reports.

2.1givenstep 1.5algebra∎

Every dimension-bounded loop terminates after finitely many operations, and the outer loop increments its update counter until it reaches the finite cap N or returns an earlier status. Therefore every invocation terminates with one of its declared statuses. A tolerance status based on nonzero residuals is explicitly residual-candidate, so no step above treats it as a certificate.

Depends on

Used by

Dependency tree · two levels

26 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources