📐algorithm-gradient-descent-quadratic-tests

The groupant beside algorithms/src/gradient_descent_quadratic/tests.rs in the Mova repository: what the code computes, what it never does, how to go back from its outputs, its invariants, cost and checks.

Facts

Components

Obernants

The typed relations around the forward model: the behaviour complement (what the code never does), the reverse reconstruction (every input compatible with an output) and specialised views.

Model text

descriptant

gradient_descent_quadratic tests.rs

Original standard-library numerical implementation and finite tests. Prospective predictions were saved before code in ../prospective.md.

For q(x)=half x'Dx-b'x, use g=Dx-b and x_next=x-g/L with L=max D. In exact arithmetic coordinate errors contract by 1-D_i/L in [0,1). Fresh Euclidean gradient norm is the stopping observation, not coordinate error.

19 diagonal spectra compared to analytic coordinate divisions b_i/D_i; fresh residual check, zero budget, already stationary input, ill-conditioned representational stagnation, shapes and invalid coefficients.

Literature: Burden and Faires, Numerical Analysis (bisection); Kiefer (1953), Sequential minimax search for a maximum (golden section); Nocedal and Wright, Numerical Optimization, second edition, chapter 3 (gradient descent); Hestenes and Stiefel (1952), Methods of conjugate gradients for solving linear systems (CG). No source code was copied.

2026-10-30 epoch: scoped safe bounds and explicit failure statuses were retained; finite observations do not establish universal convergence.

model

Mathematical combiner

Inputs: authoritative tests.rs, partial syntax projection, forward model, typed complement and reconstruction relations, and named finite observations. The combiner intersects accepted-domain constraints with update invariants, then partitions outputs by convergence, budget, stagnation or error. Cost is O(n*k) arithmetic, O(n) auxiliary memory through checked vector reservations; allocation failures are explicit. Tests are sibling sources, never implementation children. Source hashes bind this descriptive epoch; syntax is not semantic AST or proof.

Observation retained: 19 diagonal spectra compared to analytic coordinate divisions b_i/D_i; fresh residual check, zero budget, already stationary input, ill-conditioned representational stagnation, shapes and invalid coefficients.

Limits: callback determinism/continuity/unimodality and global SPD are assumptions; residual stopping alone says nothing universal about solution error. Conditioning, underflow, finite rounding and callback cost remain material. No speed measurements or universal accuracy certificate are claimed.

approximant

Forward approximant and invariant

Domain: Dimension 1..256, diagonal in [1e-100,1e100], finite linear and initial coordinates bounded by 1e100; positive finite gradient norm tolerance; budget <=100000.

For q(x)=half x'Dx-b'x, use g=Dx-b and x_next=x-g/L with L=max D. In exact arithmetic coordinate errors contract by 1-D_i/L in [0,1). Fresh Euclidean gradient norm is the stopping observation, not coordinate error.

Cost: O(n*k) arithmetic, O(n) auxiliary memory through checked vector reservations; allocation failures are explicit.

Test source maps fixed fixtures to assertion success/failure. 19 diagonal spectra compared to analytic coordinate divisions b_i/D_i; fresh residual check, zero budget, already stationary input, ill-conditioned representational stagnation, shapes and invalid coefficients.

Numerical claims concern additive interval widths or absolute gradient/residual norms. A relative coordinate error has undefined behavior at zero without a separate convention and is not claimed. No supported 1/2/5/10% executable surrogate is attached: this exact API is already iterative, and no independently measured faster transformation with a useful certified metric is supplied.

models

Typed obernant family

behavior_complement

Let U be all possible fixture outcomes and R the conjunction of the explicit assertions. U\R includes a wrong coordinate even if status looks plausible, false convergence, accepted invalid fixtures or exceeded callback count. Passing tests establishes membership only for these finite inputs, not all inputs.

reverse_reconstruction

A passing assertion report maps back to all implementations agreeing on the finite fixtures. This is a large nonunique fiber: unsampled callbacks, matrix spectra and resource failures remain free. Domain: Dimension 1..256, diagonal in [1e-100,1e100], finite linear and initial coordinates bounded by 1e100; positive finite gradient norm tolerance; budget <=100000.

independent_oracle

19 diagonal spectra compared to analytic coordinate divisions b_i/D_i; fresh residual check, zero budget, already stationary input, ill-conditioned representational stagnation, shapes and invalid coefficients.

mutation_sensitivity

A compiled production mutation must fail the named assertion diagonal coordinate oracle; restoring exact source bytes must recover a passing focused suite. This shows local detection power only.

boundary_partition

Partition observations by valid convergence, zero/exhausted budget, representational stagnation, and invalid inputs/nonfinite values. Allocation failure is modeled but not injected. Strict continuity/unimodality/SPD are not established by finite tests.

tests

Checks

Run cargo test -p mova-algorithms gradient_descent_quadratic::tests and its release equivalent. 19 diagonal spectra compared to analytic coordinate divisions b_i/D_i; fresh residual check, zero budget, already stationary input, ill-conditioned representational stagnation, shapes and invalid coefficients.

Compiled negative control must hit diagonal coordinate oracle. Full algorithms suite runs twice per profile; normalized ordered test outcomes are compared. Strict all-target Clippy and focused rustfmt check complete validation. See validation.md for observed evidence.

validation

Validation evidence

2026-10-30. External records under $MOVA_DATA/runs/octopus-wave19-solvers-2026-10-30/. Two debug and two release runs each passed 501 library tests (12 new focused tests); sorted named outcomes agree across four runs. Strict algorithms all-target Clippy and focused rustfmt passed.

Source SHA-256: fb1292d57709475d0d75146a7db40bd27c2c1ec44d8ffe2ca3705c7b45391e22. Negative production mutant failed diagonal coordinate oracle; restored bytes passed the focused suite.

• negative-gradient_descent_quadratic.log SHA-256 155d6eb36448f4ab36eb6b7d2924c5083e14e4d858d3277e976f9f065b35bc73
• restored-gradient_descent_quadratic.log SHA-256 de6de777c098e634fee1a7039f978a4798a261239f2fa86882312fdf8abf349c
• debug1.log SHA-256 61428a2e0c6dbc43cde2fa1cf9a4b021a6cebd75c04de1ecb3cf761fa95eccbf
• debug2.log SHA-256 73ffe6afebfbf497bd448639d77d254de8142e4c170e346e976eff376e95e496
• release1.log SHA-256 9a28e6d7f1af6cfbf056323b4ec824aca1899d9ed52b1635d30ed01ed8785afb
• release2.log SHA-256 17cdc616d33e1ca8add5062ebed8e5303eebab267338c981dbc2aecba57afd43
• clippy.log SHA-256 71ef179d48cc2d1e07e85d2e77009ed79e6e543be1e409886afdb92b50363422

Partial syntax projection generated independently twice and compared byte-for-byte. These are finite checks; allocation exhaustion and arbitrary caller functions/matrices are not exhaustively verified.

This page in Markdown → https://md.groupants.elamur.ai/free/en/mova/algorithms/src/gradient_descent_quadratic/tests.rs.groupant/