ThotDjehuty
d780ed81d7
Adds 9 new top-level / sub-modules to the Rust API only (no Python
bindings yet), with at least one analytic unit test per module.
New Rust modules:
- optimal_control::matrix_riccati (RK4 backward solver)
- timeseries_utils::nonsync_covariance (Hayashi-Yoshida)
- timeseries_utils::wavelet (Haar / Daubechies DWT and MODWT)
- risk_measures (VaR, CVaR, projected sub-gradient CVaR minimisation)
- graph::laplacian + graph::spectral_clustering (Jacobi + k-means++)
- topology (Vietoris-Rips persistent homology, bottleneck distance)
- volterra (Caputo Adams, Markovian lift, second-kind Volterra,
Fourier inversion of characteristic functions)
- signatures (truncated tensor signature, log-sig, random reservoir,
Salvi-Cass-Lyons signature kernel, shuffle product)
All previously stable APIs untouched; abi3-py38 ABI preserved.
New module tests: 29/29 passing. Pre-existing 5 unrelated failures
unchanged.
43 lines
1.0 KiB
TOML
43 lines
1.0 KiB
TOML
[package]
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name = "optimiz-rs"
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version = "1.1.0"
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edition = "2021"
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authors = ["HFThot Research Lab <contact@hfthot-lab.eu>"]
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description = "High-performance optimization algorithms in Rust with Python bindings"
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license = "MIT"
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repository = "https://github.com/ThotDjehuty/optimiz-r"
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keywords = ["optimization", "machine-learning", "statistics", "numerical", "scientific"]
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categories = ["algorithms", "science", "mathematics"]
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readme = "README.md"
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[lib]
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name = "optimizr"
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crate-type = ["cdylib", "rlib"]
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[dependencies]
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pyo3 = { version = "0.21", features = ["extension-module", "abi3-py38"], optional = true }
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numpy = { version = "0.21", optional = true }
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rand = "0.8"
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rand_distr = "0.4"
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ndarray = "0.15"
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ndarray-linalg = { version = "0.16", features = ["openblas-system"] }
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num-traits = "0.2"
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rayon = "1.8"
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thiserror = "1.0"
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ordered-float = "4.2"
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statrs = "0.17"
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[features]
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default = []
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python-bindings = ["pyo3", "numpy"]
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parallel = []
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[dev-dependencies]
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criterion = "0.5"
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approx = "0.5"
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[profile.release]
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opt-level = 3
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lto = true
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codegen-units = 1
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