Elamur Groupantsalgorithm-sinkhorn-scaling-tests
The groupant beside algorithms/src/sinkhorn_scaling/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
Implementation material. A Mova code model describes this code at one source revision: partial models, finite checks and their stated scope. It is not a reviewed general exposition, and its labels are shown as written, never raised.
Components
| component | label | where |
|---|---|---|
| approximant | partial | approximant |
| ast | partial | syntax.json |
| descriptant | present | descriptant |
| invariant | partial | model |
| tests | existing checks referenced | tests |
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.
| id | type | label | where |
|---|---|---|---|
behavior_complement | behavior_complement | partial source derived model | behavior_complement |
reverse_reconstruction | reverse_reconstruction | partial source derived model | reverse_reconstruction |
independent_oracle | independent_oracle | partial source derived model | independent_oracle |
boundary_partition | boundary_partition | partial source derived model | boundary_partition |
compiled_mutation | compiled_mutation | partial source derived model | compiled_mutation |
Model text
descriptant
sinkhorn_scaling checks
Original std-only implementation and fixtures; source citations appear in module documentation.
Independent finite oracle and boundary tests for sinkhorn_scaling. Production: Dimensions1..64; all matrix and marginal values finite in[1e-100,1e100]. Shape then finite positive tolerance/budget<=10000 then values then marginal mass agreement within1e-12 relative. Alternate row scaling and column scaling. Report max absolute marginal discrepancy, iteration count and convergence flag.
Epoch 2026-10-30: independent tiny-domain oracle, repeated debug/release and compiled production mutation. No timing or end-to-end Mova claims.
Validation refinement: the initial rank-one fixture selected row targets proportional to its input row factors, so a compiled mutant omitting row scaling survived that oracle. The fixture now uses reversed row targets, independently of input factors; this mutant must fail convergence. Initial evidence is preserved in the external initial-validation directory. This repairs an oracle blind spot, not a production recurrence.
model
Invariants and state
Independent finite oracle and boundary tests for sinkhorn_scaling. Production: Dimensions1..64; all matrix and marginal values finite in[1e-100,1e100]. Shape then finite positive tolerance/budget<=10000 then values then marginal mass agreement within1e-12 relative. Alternate row scaling and column scaling. Report max absolute marginal discrepancy, iteration count and convergence flag.
All-pass excludes represented oracle mismatches and rejected boundary violations in these finite fixtures only. Production excluded-output relation: Converged excludes residual>tolerance, not large distance to a limit or failure of an optimization objective. Budget status excludes extra iterations. Nonpositive/nonfinite computed cells exclude successful output.
O(Krc) time, O(r*c) space, K<=10000. Standard allocation; no callbacks.
All allocations follow standard Rust process policy, not fallible allocation APIs. No callbacks or external effects. f64 roundoff and underflow remain explicit limitations.
approximant
Forward and cost approximant
Independent finite oracle and boundary tests for sinkhorn_scaling. Production: Dimensions1..64; all matrix and marginal values finite in[1e-100,1e100]. Shape then finite positive tolerance/budget<=10000 then values then marginal mass agreement within1e-12 relative. Alternate row scaling and column scaling. Report max absolute marginal discrepancy, iteration count and convergence flag.
O(Krc) time, O(r*c) space, K<=10000. Standard allocation; no callbacks.
No executable cheaper surrogate or supported generic 1/2/5/10% profile is claimed. Residuals and floating estimates are not solution-error certificates.
models
behavior_complement
All-pass excludes represented oracle mismatches and rejected boundary violations in these finite fixtures only. Production excluded-output relation: Converged excludes residual>tolerance, not large distance to a limit or failure of an optimization objective. Budget status excludes extra iterations. Nonpositive/nonfinite computed cells exclude successful output.
reverse_reconstruction
All-pass observations admit implementations differing on unsampled inputs. This finite test fiber does not uniquely identify source or prove universality. Production fiber: Positive diagonal row/column rescalings share cross ratios and may converge to the same balanced matrix; original matrix and scaling factors are not recoverable uniquely. At budget0 the exact input matrix is retained.
independent_oracle
Tiny path enumeration, analytical rank-one matrices or independent weighted moments reconstruct expectations without calling the production recurrence; scope is finite.
boundary_partition
Shape, zero/empty, invalid finite values, ties or budget boundaries are explicitly asserted. Numeric tolerance is fixture-specific.
compiled_mutation
A compiled changed production recurrence must fail the named oracle assertion. Restoration plus positive rerun checks that the gate can distinguish this wrong behavior.
tests
Validation
Run cargo test -p mova-algorithms sinkhorn_scaling::tests. Evidence in $MOVA_DATA/runs/octopus-wave26-probability-2026-10-30: repeated profiles, mutation logs and restored positive runs. Three named tests per module. See receipt.json for evidence hashes. No allocator fault injection, timing or full-school measurement.
prospective
Probability batch prospective math
Written before implementation. All code original, std-only. Bounded allocations use standard Vec; allocator failure follows Rust process policy, no callbacks or RNG.
Alias: Walker (1977), An efficient method for generating discrete random variables with general distributions; Vose (1991), A linear algorithm for generating random numbers with a given distribution. n 1..4096, finite nonnegative weights, positive maximum. Scale by maximum then total to avoid overflow. Small/large work lists conserve total scaled mass. Column uniform and coin uniform [0,1) produce the specified categorical distribution in real arithmetic; caller explicitly supplies coordinates. O(n) build/storage, O(1) draw. No probabilistic draw generation or numeric percentage guarantee.
HMM: Rabiner (1989), A tutorial on hidden Markov models and selected applications in speech recognition; Viterbi (1967), Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. n1..64, T0..4096; finite probabilities in [0,1], initial/transition rows sum to one within 1e-10. Emissions time-major likelihoods in [0,1], need not sum across states. Validate structure then numeric values before empty sequence. Empty log likelihood0/path empty. Viterbi log max-plus recurrence, predecessor ties lowest index and terminal ties lowest index (not global lexicographic). No positive path =>None. Forward/backward log-sum-exp, posterior proportional exp(alpha+beta-logZ); per-row normalization corrects rounding. O(Tn²) time/O(Tn) space. Tiny exhaustive state-sequence enumeration is independent oracle. Log scales avoid direct-product underflow; no general floating error bound.
Sinkhorn: Sinkhorn and Knopp (1967), Concerning nonnegative matrices and doubly stochastic matrices. Positive matrix entries and marginals restricted [1e-100,1e100], dims1..64, equal marginal totals within tolerance1e-12 relative. Alternate row and column multiplication to target marginals; residual max absolute marginal discrepancy. Budget0..10000, finite tolerance>0. Stop on residual<=tolerance or budget; overflow/underflow to zero explicit arithmetic error. O(iterationsrc) time/O(rc) space. Residual is feasibility evidence, not solution distance/optimality guarantee. Scale-invariant input fibers and budget status explicit.
Weighted covariance: West (1979), Updating mean and variance estimates: an improved method; Chan, Golub, LeVeque (1983), Algorithms for computing the sample variance. dimension1..64; finite coordinates |x|<=1e100, weights[0,1e100]. Population covariance M2/W, not unbiased sample covariance. Zero weight validates coordinates then does nothing. Merge dimensions must match, empty neutral. Merge W=A+B; delta=muB-muA; mu=muA+deltaB/W; M2=M2A+M2B+delta outer deltaA*(B/W). Atomic error updates, finite intermediates required. O(d²) push/merge/storage. Independent batch weighted centered moments oracle, partition merge equivalence within tolerance. No order-bitwise identity or universal covariance error certificate.
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