Displaying 1 to 7 from 7 results

## tf-quant-finance - High-performance TensorFlow library for quantitative finance.

•    Jupyter

This library provides high-performance components leveraging the hardware acceleration support and automatic differentiation of TensorFlow. The library will provide TensorFlow support for foundational mathematical methods, mid-level methods, and specific pricing models. The coverage is being rapidly expanded over the next few months. Foundational methods. Core mathematical methods - optimisation, interpolation, root finders, linear algebra, random and quasi-random number generation, etc.

## fmin - Unconstrained function minimization in Javascript

•    Javascript

Unconstrained function minimization in javascript. This package implements some basic numerical optimization algorithms: Nelder-Mead, Gradient Descent, Wolf Line Search and Non-Linear Conjugate Gradient methods are all provided.

## mpmath - Python library for arbitrary-precision floating-point arithmetic

•    Python

A Python library for arbitrary-precision floating-point arithmetic. Numerous other people have contributed by reporting bugs, requesting new features, or suggesting improvements to the documentation.

## Parametron.jl - Efficiently solving parameterized families of optimization problems in Julia

•    Julia

Parametron makes it easy to set up and efficiently (ideally, with zero allocation) solve instances of a parameterized family of optimization problems. with decision variable vector x, and where A, b, C, and d are parameters with random values, to be re-sampled each time the problem is re-solved.

## ForBES - Generic and efficient MATLAB solver for nonsmooth optimization problems

•    Matlab

ForBES (standing for Forward-Backward Envelope Solver) is a MATLAB solver for nonsmooth optimization problems. It is generic in the sense that the user can customize the problem to solve in an easy and flexible way. It is efficient since it features very efficient algorithms, suited for large scale applications.

## autodiff - Autodiff is a numerical library for the Go programming language

•    Go

Autodiff is a numerical optimization and linear algebra library for the Go / Golang programming language. It implements basic automatic differentation for many mathematical routines. The documentation of this package can be found here. A scalar holding the value 1.0 can be defined in several ways, i.e.

## argmin - [WIP] Mathematical optimization in pure Rust

•    Rust

This crate's intention is to be useful to users as well as developers of optimization algorithms, meaning that it should be both easy to apply and easy to implement algorithms. In particular, as a developer of optimization algorithms you should not need to worry about usability features (such as logging, dealing with different types, setters and getters for certain common parameters, counting cost function and gradient evaluations, termination, and so on). Instead you can focus on implementing your algorithm. Since this crate is in a very early stage, so far most points are only partially implemented or remain future plans.

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