# SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

arXiv: https://arxiv.org/abs/1907.10121 · HTML: https://mlsanity.com/paper/1907.10121/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 30 of 49)

- [Emergent Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2212.09196/) (2022)
- [ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning](https://mlsanity.com/paper/2212.07919/) (2022)
- [Evaluating Human-Language Model Interaction](https://mlsanity.com/paper/2212.09746/) (2022)
- [Global Vision Transformer Pruning with Hessian-Aware Saliency](https://mlsanity.com/paper/2110.04869/) (2021)
- [Distillation Scaling Laws](https://mlsanity.com/paper/2502.08606/) (2025)
- [Real-Time Radiance Fields for Single-Image Portrait View Synthesis](https://mlsanity.com/paper/2305.02310/) (2023)
- [Distributed Deep Learning in Open Collaborations](https://mlsanity.com/paper/2106.10207/) (2021)
- [On The Effect of Auxiliary Tasks on Representation Dynamics](https://mlsanity.com/paper/2102.13089/) (2021)
- [Large Language Models as Superpositions of Cultural Perspectives](https://mlsanity.com/paper/2307.07870/) (2023)
- [SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning](https://mlsanity.com/paper/2207.04606/) (2022)
- [An Extensible Benchmark Suite for Learning to Simulate Physical Systems](https://mlsanity.com/paper/2108.07799/) (2021)
- [Arbitrary Decisions are a Hidden Cost of Differentially Private Training](https://mlsanity.com/paper/2302.14517/) (2023)
- [Precision Machine Learning](https://mlsanity.com/paper/2210.13447/) (2022)
- [Transformer-Based Learned Optimization](https://mlsanity.com/paper/2212.01055/) (2022)
- [Quantum neural network autoencoder and classifier applied to an industrial case study](https://mlsanity.com/paper/2205.04127/) (2022)
- [Point normal orientation and surface reconstruction by incorporating isovalue constraints to Poisson equation](https://mlsanity.com/paper/2209.15619/) (2022)
- [Closed-form geodesics and trust-region method to calculate Riemannian logarithms on Stiefel and its quotient manifolds](https://mlsanity.com/paper/2103.13327/) (2021)
- [Time Series Forecasting Using Manifold Learning](https://mlsanity.com/paper/2110.03625/) (2021)
- [What's Behind the Couch? Directed Ray Distance Functions (DRDF) for 3D Scene Reconstruction](https://mlsanity.com/paper/2112.04481/) (2021)
- [Sentiment Analysis of Persian-English Code-mixed Texts](https://mlsanity.com/paper/2102.12700/) (2021)
- [Principled Acceleration of Iterative Numerical Methods Using Machine Learning](https://mlsanity.com/paper/2206.08594/) (2022)
- [Learning suction graspability considering grasp quality and robot reachability for bin-picking](https://mlsanity.com/paper/2111.02571/) (2021)
- [Visual Referential Games Further the Emergence of Disentangled Representations](https://mlsanity.com/paper/2304.14511/) (2023)
- [Efficiently Learning the Graph for Semi-supervised Learning](https://mlsanity.com/paper/2306.07098/) (2023)
- [Stochastic optimal well control in subsurface reservoirs using reinforcement learning](https://mlsanity.com/paper/2207.03456/) (2022)
- [Riemannian optimization for non-centered mixture of scaled Gaussian distributions](https://mlsanity.com/paper/2209.03315/) (2022)
- [A deep-learning search for technosignatures of 820 nearby stars](https://mlsanity.com/paper/2301.12670/) (2023)
- [Symplectic Momentum Neural Networks -- Using Discrete Variational Mechanics as a prior in Deep Learning](https://mlsanity.com/paper/2201.08281/) (2022)
- [Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides](https://mlsanity.com/paper/2205.01931/) (2022)
- [Towards a Better Understanding of Representation Dynamics under TD-learning](https://mlsanity.com/paper/2305.18491/) (2023)

## Abstract (arXiv)

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.