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Changelog

All notable changes to PyChebyshev will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.9.5] - 2026-02-15

Added

  • Sub-interval integration via bounds parameter on integrate() for both ChebyshevApproximation and ChebyshevSpline. Uses generalized Fejér-1 quadrature with sub-interval Chebyshev moments computed from closed-form antiderivatives (Trefethen 2013, Ch. 19).
  • Spline sub-interval integration with automatic piece overlap clipping: pieces with no overlap are skipped, partial overlaps receive sub-interval bounds, fully contained pieces use standard Fejér-1 weights.
  • 22 new tests for sub-interval integration (14 for ChebyshevApproximation, 8 for ChebyshevSpline), including polynomial exactness, scipy cross-validation, additivity, and error handling.

[0.9.0] - 2026-02-15

Added

  • integrate() method on ChebyshevApproximation and ChebyshevSpline for definite integration via Fejér-1 quadrature (Waldvogel 2006), with O(n log n) weight computation via DCT-III and O(n^d) multi-D contraction via BLAS.
  • roots() method on ChebyshevApproximation and ChebyshevSpline for finding all real roots along a dimension via companion matrix eigenvalues (numpy.polynomial.chebyshev.chebroots).
  • minimize() / maximize() methods on ChebyshevApproximation and ChebyshevSpline for global optimization via derivative rootfinding and endpoint evaluation.
  • Partial integration returns a lower-dimensional ChebyshevApproximation (analogous to slice()).
  • Internal _calculus.py module with shared helpers for Fejér-1 weights, 1-D rootfinding, and 1-D optimization.
  • New documentation page: Chebyshev Calculus (integration, roots, optimization).
  • 52 new tests for calculus operations (test_calculus.py).

[0.8.0] - 2026-02-15

Added

  • extrude() and slice() methods on ChebyshevApproximation, ChebyshevSpline, and ChebyshevSlider.
  • Extrusion adds new dimensions where the function is constant, enabling portfolio combination across different risk-factor sets.
  • Slicing fixes a dimension at a value, reducing dimensionality via barycentric interpolation (with fast path at exact nodes).
  • Extrude-then-slice round-trip preserves the original interpolant exactly.
  • Internal _extrude_slice.py module with shared validation and tensor manipulation helpers.
  • New documentation page: Extrusion & Slicing (mathematical proofs, API reference, portfolio use case).
  • compare_extrude_slice.py -- MoCaX comparison script (local only, not in CI).
  • 63 new tests for extrusion and slicing across all three classes.

[0.7.0] - 2026-02-14

Added

  • Arithmetic operators (+, -, *, /, unary -, +=, -=, *=, /=) for ChebyshevApproximation, ChebyshevSpline, and ChebyshevSlider.
  • Portfolio-level combination of Chebyshev interpolants via weighted sums -- no re-evaluation needed.
  • Derivatives and error estimates propagate correctly through algebraic operations.
  • Internal _algebra.py module with shared compatibility validation.
  • _from_grid() factory on ChebyshevApproximation, _from_pieces() on ChebyshevSpline, _from_slides() on ChebyshevSlider.

[0.6.0] - 2026-02-14

Added

  • ChebyshevSpline class for piecewise Chebyshev interpolation with user-specified knots (Section 3.8, Ruiz & Zeron 2021)
  • Knot-based domain partitioning: place knots at kinks or discontinuities to restore spectral convergence on each piece
  • eval(), eval_multi(), eval_batch() with automatic piece routing via np.searchsorted
  • Analytical derivatives within each piece via spectral differentiation matrices (inherited from ChebyshevApproximation)
  • ValueError raised when requesting derivatives at knot boundaries (left/right derivatives differ)
  • error_estimate() returning the maximum error across all disjoint pieces
  • save() / load() serialization following the same pattern as other PyChebyshev classes
  • num_pieces, total_build_evals, build_time properties
  • New documentation page: Chebyshev Splines (mathematical justification, usage guide)
  • compare_spline.py — MoCaX spine comparison script (local only, not in CI)
  • ~34 new tests for spline construction, accuracy, derivatives, batch eval, serialization

[0.5.0] - 2026-02-12

Added

  • ChebyshevTT class for Chebyshev interpolation in Tensor Train format (Chapter 6, Ruiz & Zeron 2021)
  • TT-Cross approximation (method='cross') for building from O(d * n * r^2) function evaluations instead of O(n^d)
  • TT-SVD build option (method='svd') for validation and moderate-dimension problems
  • eval() for single-point evaluation via TT inner product with Chebyshev polynomial contraction
  • eval_batch() for vectorized batch evaluation of multiple points simultaneously via numpy einsum
  • eval_multi() for finite-difference derivatives (1st and 2nd order, including cross-derivatives)
  • error_estimate() for approximate error estimation from Chebyshev coefficient cores
  • save() / load() serialization with version compatibility checks (same pattern as other classes)
  • tt_ranks and compression_ratio properties for inspecting the TT decomposition
  • _maxvol() module-level function implementing the maximum-volume row selection algorithm
  • _tt_cross() module-level function implementing TT-Cross with alternating left-right sweeps and maxvol pivot selection

[0.4.0] - 2026-02-10

Added

  • error_estimate() method on ChebyshevApproximation — estimates supremum-norm interpolation error from Chebyshev expansion coefficients via DCT-II, without needing the true function (Ruiz & Zeron 2021, Section 3.4)
  • error_estimate() method on ChebyshevSlider — returns sum of per-slide error estimates
  • _chebyshev_coefficients_1d() static method for computing Chebyshev expansion coefficients from values at Type I nodes
  • Error estimate shown in __str__() output for both classes when built
  • New documentation page: Error Estimation (mathematical background, usage examples)
  • 12 new tests for error estimation (8 for barycentric, 4 for slider)
  • compare_error_estimation.py — local benchmarking script comparing PyChebyshev vs MoCaX error estimates

[0.3.0] - 2026-02-10

Deprecated

  • fast_eval() — use vectorized_eval() instead, which is ~150x faster via BLAS GEMV
  • [jit] optional dependency (Numba) — no longer needed since BLAS path outperforms JIT
  • _jit.py module — will be removed in a future version

Changed

  • README, docs, and CLAUDE.md updated to reflect BLAS GEMV as the primary fast path
  • Removed Numba JIT installation instructions from all documentation
  • Removed numba from dev dependencies

[0.2.1] - 2026-02-10

Added

  • save() and load() methods on ChebyshevApproximation and ChebyshevSlider for persisting built interpolants to disk (pickle-based)
  • __repr__ and __str__ methods on both classes for human-readable printing
  • Version compatibility check on load with warning for mismatched versions
  • New documentation page: Saving & Loading Interpolants
  • 21 new tests for serialization and printing

[0.2.0] - 2026-02-09

Added

  • ChebyshevSlider class for high-dimensional approximation via the Sliding Technique (Ch. 7, Ruiz & Zeron 2021)
  • Additive decomposition into low-dimensional slides around a pivot point
  • Analytical derivatives per slide with correct cross-group mixed partial handling (returns 0)
  • Documentation page for the Sliding Technique with usage examples and limitations
  • 24 new tests for slider (additive, coupled, 5D, cross-group derivatives, validation)

Changed

  • README updated: repositioned as a library (was standalone educational script)
  • Getting Started section now uses pip install pychebyshev with code examples
  • Fixed repo URL in docs (maxjingwezhang → 0xC000005)

[0.1.1] - 2026-02-07

Fixed

  • README images now load on PyPI (use absolute GitHub URLs instead of relative paths)

[0.1.0] - 2026-02-07

Added

  • ChebyshevApproximation class for multi-dimensional Chebyshev tensor interpolation
  • Barycentric interpolation with full weight pre-computation
  • Analytical derivatives via spectral differentiation matrices (1st and 2nd order)
  • vectorized_eval() using BLAS matrix-vector products (~0.065 ms/query)
  • vectorized_eval_multi() with shared barycentric weights (~0.29 ms for price + 5 Greeks)
  • fast_eval() with Numba JIT compilation (optional)
  • Node coincidence handling for all evaluation methods
  • MkDocs + Material documentation with KaTeX math rendering
  • pytest test suite (22 tests covering 3D/5D accuracy and method consistency)
  • GitHub Actions CI/CD for testing and PyPI publishing