resona 1.0.0

The FFT of operators: probe, compose, read — matrix-free spectral computation with a built-in cost law.

resona — the FFT of operators

fft(x) takes a signal to the basis where convolution becomes pointwise multiply. resona.of(A) takes an operator (anything that can multiply a vector) to the representation where composition becomes addition — and from which every spectral question is answered. No matrix is ever formed; eig is never called.

In plain words: you have a thing that transforms vectors — a matrix too big to write down, a graph, a physics simulation, a neural network's Hessian, a quantum Hamiltonian. resona listens to it, tells you everything about its spectrum, lets you add and multiply such things without ever building them, apply functions of them to vectors, measure how hard your problem truly is — and even design new operators to order.

pip install resona          # numpy + scipy only

60 seconds

import numpy as np, resona

A = np.random.default_rng(0).standard_normal((3000, 3000)); A = A @ A.T / 3000
matvec = lambda v: A @ v        # ← all resona ever needs: v ↦ Av

s = resona.of(matvec, 3000)     # PROBE  — "ring" the operator, hear its spectrum
s.extreme()                     # smallest & largest eigenvalue
s.trace(np.log)                 # log-determinant — no Cholesky, no eig
s.density(np.linspace(0, 4, 200))   # the spectrum's shape
s.effective_rank()              # the cost dial: is this problem cheap or hard?

t = resona.of(lambda v: A @ (A @ v), 3000)
(s + t).extreme()               # spectrum of A + A², A + A² never formed

b = np.random.standard_normal(3000)
mv1 = lambda v: A @ v + v                        # A + I (well-conditioned)
x = resona.apply(mv1, lambda lam: 1/lam, b)      # solve (A+I)x = b, matrix-free

Every line above is matrix-free: the cost is the matvec, not O(N³).

Choose your door

🚶 New to operators / numerics? Take the tour — ten stops from "what is a matvec" to designing your own operators, every stop in plain words first, the math second.

🎓 Mathematician? The library is a dictionary of theorems made executable — each entry verified in tests/ and examples/ against dense ground truth:

you call	which is	verified
resona.of(mv, N)	stochastic Lanczos quadrature = Gauss quadrature of the spectral measure	moments vs dense, 35-operator suite
s + t, s @ t	exact closure (A+B)x = Ax+Bx; at the measure level: free convolution ⊞/⊠ (Voiculescu)	extreme eig of A+B to 1e-9
s.boxplus(t)	κₙ(A⊞B) = κₙ(A)+κₙ(B) (Speicher, non-crossing partitions); as_spectral=True → Golub–Welsch	semicircle ⊞ semicircle exact
s.r(w) / s.s(w)	R-transform (linearizes ⊞ — "its Cole–Hopf", our framing, see NOVELTY) / S-transform (⊠)	additivity to 0.5%
s.flow(t, xs)	free heat flow = inviscid complex Burgers; shock_time = band merger	t_c ≈ 1 for atoms ±1
subordination.pastur_grid	the Pastur subordination fixed point g = G_A(z−σ²g)	m₂ vs Monte-Carlo
s.extreme()	BBP transition & Tracy–Widom fluctuations live here	λ=θ+1/θ above θ_c; measured exp −0.65 (theory −⅔)
defect.pseudospectrum_radius	the ε^{1/q} bloom of an order-q Jordan defect; GMRES follows Λ_ε	exact on J_q, q=2…5
solve.catastrophe_solve	Arnold A_{q−1} stratum ⇒ float64 keeps 16/q digits; budget dps = q·target	4.9 → 17.0 digits
cost.level_spacing_ratio	⟨r⟩: Poisson 0.386 (integrable) vs GOE 0.531 (chaotic)	0.392 / 0.532 on XXZ±NNN
lift.conserved_charge	commutator-Gram eigenproblem: near-kernel = integrals of motion	finds H, ΣZ, bilinears blind
lift.carleman_*	Carleman linearization; over GF(p), x^p≡x makes ANY logic exactly linear	0 errors on all pⁿ inputs
from_measure / from_eigenbasis	the inverse spectral problem (Stieltjes / Jacobi); synthesis of operators to order	eig = order to 5.6e-15
s.effective_rank()	Φ₁ participation ratio; the Extraction-Law cost dial	dequantization boundary (Tang)

🔧 Have a task right now? The cookbook: find your task in the "I want to…" table, copy the recipe.

What it solves (matrix-free, one primitive)

Verified against dense ground truth in examples/ — 44 gallery scripts, every metric printed by the script itself:

task	what	metric
GP log-determinant	log|K| for hyperparameter learning at scale	0.84% rel.err, no Cholesky
Loss-Hessian spectrum	sharpness & curvature from HVPs, no Hessian formed	λ_max 0.00%, Tr 0.30%
Spectrum of A+B	composed, matrix-free (Horn's problem in practice)	extreme eig to 1e-9
Deep-net trainability	cond(W_L…W_1) predicted from init, no fwd/bwd	Gaussian explodes, orthogonal ≈1
Effective rank Φ₁	the cost dial: structured/cheap vs full/frontier	14 vs 466
Nonlinear PDE (Burgers)	lift to linear (Cole–Hopf) → exp(tK)·v	residual 5e-9, matrix-free
35 operators → spectra	matrix-free Ritz seed → Rayleigh polish	seed 1e-4 → 1e-16, 100% machine-zero
Operator synthesis	order a spectrum → get a working local matvec	eig = order to 5.6e-15
GMRES stall prediction	same spectrum, opposite fates — the pseudospectrum knows	14 iters vs stall, read from σ_min
Is it integrable?	⟨r⟩ + blind conserved-charge search	0.392/0.532; 4 charges vs 1
Signal in noise (BBP)	does a spike detach from the bulk?	λ=θ+1/θ above θ_c=1
Anderson localization	metal→insulator from disorder, matrix-free	Λ: 0.97→0.15 in 3.4s
Tracy–Widom edge	the universal fluctuation law of extreme()	std·N^⅔→1.27, measured exp −0.65 (theory −⅔)
JWST image analysis	structure map, source detection, denoising — straight from PyPI	corr 0.97 vs dense; front found

More broadly: density of states, Tr f(A) (log-det, Tr A⁻¹, partition functions, Schatten norms), extreme eigenvalues & gaps, disorder-averaged spectra, phase transitions, spectral clustering, operator design — anything that is a spectral functional of an operator you can only matvec.

The shape of the library

Three verbs on one object, everything else reads off the same hub:

  PROBE                       READ                          COMPOSE
  s = resona.of(matvec, N) →  s.trace(f) s.density(xs)      s + t   s @ t
                              s.extreme() s.moment(p)       s.boxplus(t)
                                   │
  the lifted coordinates:          │        the dials:
  s.cauchy(z) s.r(w) s.s(w)        │        s.effective_rank()  (Φ₁)
  s.cumulants()                    │        s.condition()
                                   │
  the flow:  s.flow(t, xs)  s.shock_time()      the closure:  s.levels(N)

  APPLY      resona.apply(matvec, f, v)  →  f(A)·v   (solve / evolve / filter)
  INVERSE    resona.from_measure / from_eigenbasis   (measure → operator: SYNTHESIS)
  PRECISION  resona.solve.rayleigh_polish / catastrophe_solve  (digits, only where needed)

When the matvec also maps blocks (A @ X), probing rides one BLAS-3 block-Lanczos automatically — 2–4× faster, bit-compatible (verified, then enabled; never assumed).

The deeper machinery is in plain modules — wkernel (spectral Jacobian ∂λ/∂k + design), lift (R/S transforms, Carleman, conserved charges), free (cumulants, freeness), subordination (Pastur), flow (Burgers), beta (max-entropy closure), defect (Richardson + pseudospectra), cost (Extraction Law, ⟨r⟩), solve (precision on the defect) — each documented in its docstring, each verified in tests/.

Honesty

The underlying algorithms (SLQ, Lanczos, free probability, Carleman, Golub–Welsch) are classical and credited in NOVELTY.md. resona's contribution is the single primitive + matrix-free composition algebra + the built-in cost law as one object — the way FFT organizes signal processing. The unifying claims (the Extraction Law, Φ₁-as-boundary) are research hypotheses, labelled as such; the computations are verified. Every estimate states its honest limit in its docstring: condition() is a lower bound, boxplus needs freeness, from_measure is ill-conditioned for atomic measures, catastrophe_solve cannot recover information float64 already destroyed. Stochastic reads give ~2–4 digits; machine precision is bought, where it matters, with rayleigh_polish — paying only on the defect's support.

Theory

The unified picture — the response measure as a conjugate pair, free probability (closure, the freeness boundary, the semicircle attractor), the defect = shock = edge identity, and the Extraction Law — is in THEORY.md, with reproducible scripts in theory/. The claims are calibrated in NOVELTY.md; open conjectures and the research log including the failures are in FRONTIER.md. The cost of every method is in COMPLEXITY.md.

License

MIT © 2026 Dmitry Sierikov. Attribution requested for the research contributions in NOVELTY.md.