The Evolution and Dependencies of Scientific Python Libraries

Numerical computing libraries like NumPy and SciPy rely on foundational mathematical code spanning decades. Until recently, NumPy depended on Fortran-based BLAS/LAPACK implementations for linear algebra operations. Modern versions now use OpenBLAS, which replaces Fortran code with optimized C implementations. SciPy, however, still incorporates Fortran 77 code for certain functionalities, such as ARPACK (used in eigenvalue computations) and FFTPACK (for Fourier transforms). These dependencies stem from legacy libraries like BLAS (1970s), LAPACK (1980s), and MINPACK (optimization), which remain widely used due to their mathematically stable, battle-tested algorithms like Simulated Annealing.

Simulated Annealing: A 1953 Algorithm in Modern ML

Imagine searching for the largest mushroom in a forest. Gradient methods risk settling for a local maximum, but Simulated Annealing (SciPy’s optimize) balances exploration and exploitation: early random “high-energy” steps avoid local traps, then gradually refines toward the global optimum.

Originally devised to model atomic behavior in molten metals (Metropolis Algorithm, 1953), it mimics annealing—slow cooling ensures uniform atomic arrangement. Scientists introduced probabilistic acceptance of suboptimal states to escape flawed structures. Thise method was adopted to optimize ML models, logistics, and pattern recognition, making the familiar Python code use bindings which are ~15 years older than Python itself.

Source: Facebook post (Ru)

#SciPy #Fortran #NumPy #Math

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The Evolution and Dependencies of Scientific Python Libraries

Numerical computing libraries like NumPy and SciPy rely on foundational mathematical code spanning decades. Until recently, NumPy depended on Fortran-based BLAS/LAPACK implementations for linear algebra operations. Modern versions now use OpenBLAS, which replaces Fortran code with optimized C implementations. SciPy, however, still incorporates Fortran 77 code for certain functionalities, such as ARPACK (used in eigenvalue computations) and FFTPACK (for Fourier transforms). These dependencies stem from legacy libraries like BLAS (1970s), LAPACK (1980s), and MINPACK (optimization), which remain widely used due to their mathematically stable, battle-tested algorithms like Simulated Annealing.

Simulated Annealing: A 1953 Algorithm in Modern ML

Imagine searching for the largest mushroom in a forest. Gradient methods risk settling for a local maximum, but Simulated Annealing (SciPy’s optimize) balances exploration and exploitation: early random “high-energy” steps avoid local traps, then gradually refines toward the global optimum.

Originally devised to model atomic behavior in molten metals (Metropolis Algorithm, 1953), it mimics annealing—slow cooling ensures uniform atomic arrangement. Scientists introduced probabilistic acceptance of suboptimal states to escape flawed structures. Thise method was adopted to optimize ML models, logistics, and pattern recognition, making the familiar Python code use bindings which are ~15 years older than Python itself.

Source: Facebook post (Ru)

#SciPy #Fortran #NumPy #Math

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6.4K viewsFeb 24 at 10:28