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NumPy, a BSD-licensed library that adds support for the manipulation of large, multi-dimensional arrays and matrices; it also includes a large collection of high-level mathematical functions. NumPy serves as the backbone for a number of other numerical libraries, notably SciPy. De facto standard for matrix/tensor operations in Python.
NumPy, a BSD-licensed library that adds support for the manipulation of large, multi-dimensional arrays and matrices; it also includes a large collection of high-level mathematical functions. NumPy serves as the backbone for a number of other numerical libraries, notably SciPy. De facto standard for matrix/tensor operations in Python.
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
Users can transform or summarize data by applying arbitrary functions. [4]: 132 Since Pandas is built on top of NumPy, all NumPy functions work on Series and DataFrames as well. [9]: 115 Pandas also includes built-in operations for arithmetic, string manipulation, and summary statistics such as mean, median, and standard deviation.
It is designed to follow the structure and workflow of NumPy as closely as possible and works with various existing frameworks such as TensorFlow and PyTorch. [5] [6] The primary functions of JAX are: [2] grad: automatic differentiation; jit: compilation; vmap: auto-vectorization; pmap: Single program, multiple data (SPMD) programming
Python [24] [25] with well-known scientific computing packages: NumPy, SymPy and SciPy. [26] [27] [28] R is a widely used system with a focus on data manipulation and statistics which implements the S language. [29] Many add-on packages are available (free software, GNU GPL license). SAS, [30] a system of software products for statistics.
Canonical uses of function decorators are for creating class methods or static methods, adding function attributes, tracing, setting pre-and postconditions, and synchronization, [33] but can be used for far more, including tail recursion elimination, [34] memoization and even improving the writing of other decorators. [35]
Perturbation function — any function which relates to primal and dual problems; Slater's condition — sufficient condition for strong duality to hold in a convex optimization problem; Total dual integrality — concept of duality for integer linear programming; Wolfe duality — for when objective function and constraints are differentiable ...