ThotDjehuty
d6b6018b9c
Phase 1 (top-level reorg):
- matrix_riccati promoted to crate root via re-export
- new top-level groups: bsde, pde, stochastic_control,
agent_based, inference, optimization
Phase 2 (bsde):
- theta_scheme: linear-BSDE theta-scheme
- deep_bsde_bridge: ConditionalExpectation trait + driver
Phase 3 (pde):
- fokker_planck: 1D forward FP with conservative central FD
- hjb_multid: explicit n-D HJB on Cartesian grid (d <= 3)
- elliptic_fd: 2D Poisson SOR solver
Phase 4 (stochastic_control):
- optimal_switching: Snell envelope backward induction
- pontryagin: 1D LQR Riccati shooting
- two_sided_intensity_control: bilateral intensity control
Phase 5/6 (controls):
- optimal_control::quadratic_impact_control (closed-form Riccati)
- stochastic_control::two_sided_intensity_control
Phase 7 (mean_field + agent_based):
- mean_field::mckean_vlasov: interacting-particle Euler scheme
- agent_based::mod: generic interacting-agent simulator
Phase 8 (inference + optimization):
- inference::robust_drift: Huber IRLS drift estimator
- optimization::generative_calibration_hooks: GenerativeSampler trait
+ Gaussian MMD + finite-diff calibration step
Tests: 38 NEW tests, all passing (165/170 lib total; the 5 pre-existing
failures predate v1.1 and are tracked separately).
Versions bumped: Cargo 2.0.0-alpha.1, pyproject 2.0.0a1.
Deferred to subsequent v2.0.x bumps (parallelisable follow-ups):
PyO3 bindings, executed companion notebooks, Sphinx RST pages,
hfthot-lab-instance propagation.
113 lines
5.6 KiB
Markdown
113 lines
5.6 KiB
Markdown
# Changelog
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All notable changes to **optimiz-rs** are documented in this file. The format
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follows [Keep a Changelog](https://keepachangelog.com/en/1.1.0/) and the project
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adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [2.0.0-alpha.1] - 2026-05-12
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### Added — top-level reorganisation and new generic primitives
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- **Top-level reorg (additive aliases — backward compatible at the Rust
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level).** `optimiz_rs::matrix_riccati` is now re-exported at the crate
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root. New top-level groups: `bsde`, `pde`, `stochastic_control`,
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`agent_based`, `inference`, `optimization`.
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- `bsde::theta_scheme` — implicit/explicit θ-scheme for linear BSDEs
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with deterministic coefficients (closed-form analytic test against
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the deterministic ODE `dY = -ρ Y dt`).
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- `bsde::deep_bsde_bridge` — `ConditionalExpectation` trait and
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`DeepBsdeBridge` driver providing the CPU-side recursion hook for
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external function approximators.
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- `pde::fokker_planck` — 1-D forward Fokker--Planck solver with
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conservative central differences and explicit positivity safeguard.
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- `pde::hjb_multid` — explicit upwind solver for multidimensional HJB
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on a regular Cartesian grid (`d ≤ 3`) with reflective boundaries.
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- `pde::elliptic_fd` — 2-D Poisson `-Δu = f` SOR solver verified
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against the `sin(πx) sin(πy)` eigenfunction.
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- `stochastic_control::optimal_switching` — Snell-envelope backward
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induction for discrete multi-mode optimal switching.
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- `stochastic_control::pontryagin` — Riccati-shooting solver for the
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1-D LQR Pontryagin maximum principle (verified against the
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closed-form `P(t) = s_T / (1 + s_T (T-t))`).
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- `stochastic_control::two_sided_intensity_control` — generic
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bilateral intensity control with affine per-jump premia.
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- `optimal_control::quadratic_impact_control` — closed-form Riccati
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feedback for a controlled SDE with quadratic running cost.
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- `mean_field::mckean_vlasov` — interacting-particle Euler scheme for
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generic McKean--Vlasov SDEs with empirical-measure drift.
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- `agent_based` — generic interacting-agent simulator (consensus
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dynamics test recovers the empirical mean exactly without noise).
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- `inference::robust_drift` — Huber-loss IRLS estimator for the drift
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of a 1-D OU-type discrete-time process; resists 5 % outliers.
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- `optimization::generative_calibration_hooks` — `GenerativeSampler`
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trait + Gaussian MMD loss + finite-difference calibration step.
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### Tests
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- 38 new `#[test]` cases — all passing (`cargo test --lib
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--no-default-features` passes 165/170, the 5 pre-existing failures
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predate v1.1 and are unrelated).
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### Notes — deferred to subsequent v2.0.x bumps
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- PyO3 Python bindings + executed companion Jupyter notebooks for the
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new groups (will follow the same workflow as v1.1.x).
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- Sphinx RST documentation pages for `bsde`, `pde`,
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`stochastic_control`, `agent_based`, `inference`, `optimization`.
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- Propagation of new modules into `hfthot-lab-instance` consumers.
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## [1.1.0] - 2026-05-12
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### Added — purely additive, no existing API changes
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- `optimal_control::matrix_riccati` — RK4 backward solver for the
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matrix Riccati differential equation
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`dA/dt = -2 A M A + Q`, plus terminal-condition variants for the
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associated affine and constant components.
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- `timeseries_utils::nonsync_covariance` — Hayashi--Yoshida estimator
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for asynchronous covariance, with parallel matrix variant.
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- `timeseries_utils::wavelet` — discrete and maximum-overlap wavelet
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transforms (Haar, Daubechies orders 2--10) with periodic boundaries.
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- `risk_measures` — empirical and parametric Value-at-Risk and
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Conditional Value-at-Risk estimators, plus a projected sub-gradient
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solver for convex CVaR minimisation over the unit simplex.
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- `graph::laplacian` — combinatorial, symmetric-normalised and
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random-walk graph Laplacians.
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- `graph::spectral_clustering` — spectral clustering via Jacobi
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diagonalisation and Lloyd's algorithm with k-means++ initialisation.
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- `topology::persistent_homology` — Vietoris--Rips persistent homology
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by the standard `Z/2` matrix-reduction algorithm.
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- `topology::bottleneck` — bottleneck distance between persistence
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diagrams via Hopcroft--Karp matching with binary search.
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- `volterra::fractional_riccati` — Adams predictor--corrector solver
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for Caputo fractional ODEs (Diethelm--Ford--Freed 2002).
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- `volterra::markovian_lift` — multi-exponential approximation of
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convolution kernels by non-negative least squares on a geometric
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grid.
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- `volterra::volterra_solver` — generic second-kind Volterra integral
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equation solver via product-trapezoidal quadrature.
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- `volterra::fourier_inversion` — direct trapezoidal Fourier inversion
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of a characteristic function on a uniform frequency grid.
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- `signatures::path_signature` — truncated tensor signature of a
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piecewise-linear path with truncated tensor exponential.
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- `signatures::log_signature` — truncated tensor logarithm of a
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signature.
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- `signatures::random_signature` — Cuchiero--Schmocker--Teichmann
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random reservoir projection of the signature.
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- `signatures::signature_kernel` — Salvi--Cass--Lyons signature kernel
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via the Goursat finite-difference scheme.
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- `signatures::utils` — shuffle product and Chen-identity-driven
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signature concatenation.
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### Changed
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- Bumped crate version from `1.0.1` to `1.1.0`. All previously stable
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symbols remain untouched and binary-compatible at the Python ABI
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level (`abi3-py38`).
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### Notes
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This release is **CPU-only and additive**. No Python bindings were added
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in `1.1.0`; the new modules are exposed via the Rust API only and will
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be wrapped behind the `python-bindings` feature in a follow-up release.
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