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An algorithm framework is proposed for minimizing nonsmooth functions. The framework is variable-metric in that, in each iteration, a step is computed using a symmetric positive definite matrix whose value is updated as in a quasi-Newton…

Optimization and Control · Mathematics 2019-02-05 Frank E. Curtis , Daniel P. Robinson , Baoyu Zhou

We introduce a new framework for implementing Binary Quadratic Model (BQM) solvers called Omnisolver. The framework provides an out-of-the-box dynamically built command-line interface as well as an input/output system, thus heavily reducing…

Software Engineering · Computer Science 2024-04-30 Konrad Jałowiecki , Łukasz Pawela

The progression of scientific computing resources has enabled the numerical approximation of mathematical models describing complex physical phenomena. A significant portion of researcher time is typically dedicated to the development of…

Mathematical Software · Computer Science 2015-06-22 Paul T. Bauman , Roy H. Stogner

A major challenge in the deployment of scientific software solutions is the adaptation of research prototypes to production-grade code. While high-level languages like MATLAB are useful for rapid prototyping, they lack the resource…

Mathematical Software · Computer Science 2025-12-30 Conrad Sanderson , Ryan Curtin

Last several years, GPUs are used to accelerate computations in many computer science domains. We focused on GPU accelerated Support Vector Machines (SVM) training with non-linear kernel functions. We had searched for all available GPU…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-07-21 Jan Vanek , Josef Michalek , Josef Psutka

We present SEvoBench, a modern C++ framework for evolutionary computation (EC), specifically designed to systematically benchmark evolutionary single-objective optimization algorithms. The framework features modular implementations of…

Neural and Evolutionary Computing · Computer Science 2025-05-26 Yongkang Yang , Jian Zhao , Tengfei Yang

MDL, Multimodal Deep Learning Library, is a deep learning framework that supports multiple models, and this document explains its philosophy and functionality. MDL runs on Linux, Mac, and Unix platforms. It depends on OpenCV.

Machine Learning · Computer Science 2016-04-13 Jian Jin

In this paper we present DYNAMIC, an open-source C++ library implementing dynamic compressed data structures for string manipulation. Our framework includes useful tools such as searchable partial sums, succinct/gap-encoded bitvectors, and…

Data Structures and Algorithms · Computer Science 2017-01-26 Nicola Prezza

An optimization algorithm for nonsmooth nonconvex constrained optimization problems with upper-C2 objective functions is proposed and analyzed. Upper-C2 is a weakly concave property that exists in difference of convex (DC) functions and…

Optimization and Control · Mathematics 2022-04-21 Jingyi Wang , Cosmin G. Petra

PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms.…

Generalized log-sine functions appear in higher order epsilon-expansion of different Feynman diagrams. We present an algorithm for numerical evaluation of these functions of real argument. This algorithm is implemented as C++ library with…

High Energy Physics - Phenomenology · Physics 2016-09-06 M. Yu. Kalmykov , A. Sheplyakov

This paper explores a method for solving constrained optimization problems when the derivatives of the objective function are unavailable, while the derivatives of the constraints are known. We allow the objective and constraint function to…

Optimization and Control · Mathematics 2024-02-20 Melody Qiming Xuan , Jorge Nocedal

Enhancing sampling and analyzing simulations are central issues in molecular simulation. Recently, we introduced PLUMED, an open-source plug-in that provides some of the most popular molecular dynamics (MD) codes with implementations of a…

Computational Physics · Physics 2014-10-07 Gareth A. Tribello , Massimiliano Bonomi , Davide Branduardi , Carlo Camilloni , Giovanni Bussi

Modelling of multivariate densities is a core component in many signal processing, pattern recognition and machine learning applications. The modelling is often done via Gaussian mixture models (GMMs), which use computationally expensive…

Mathematical Software · Computer Science 2018-05-10 Conrad Sanderson , Ryan Curtin

Generating high-performance CUDA kernels remains challenging due to the need to navigate a combinatorial space of low-level transformations under noisy and expensive hardware feedback. Although large language models can synthesize…

Machine Learning · Computer Science 2026-02-16 Arijit Bhattacharjee , Heng Ping , Son Vu Le , Paul Bogdan , Nesreen K. Ahmed , Ali Jannesari

A sequential quadratic optimization algorithm for minimizing an objective function defined by an expectation subject to nonlinear inequality and equality constraints is proposed, analyzed, and tested. The context of interest is when it is…

Optimization and Control · Mathematics 2023-03-01 Frank E. Curtis , Daniel P. Robinson , Baoyu Zhou

Quantum trajectory methods can be used for a wide range of open quantum systems to solve the master equation by unraveling the density operator evolution into individual stochastic trajectories in Hilbert space. This C++ class library…

Quantum Physics · Physics 2009-10-30 Ruediger Schack , Todd A. Brun

Although many active scientific codes use modern Fortran, most contemporary scientific software "libraries" are implemented in C and C++. Providing their numerical, algorithmic, or data management features to Fortran codes requires writing…

Software Engineering · Computer Science 2019-07-04 Seth R. Johnson , Andrey Prokopenko , Katherine J. Evans

We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant…

We introduce Merlion, an open-source machine learning library for time series. It features a unified interface for many commonly used models and datasets for anomaly detection and forecasting on both univariate and multivariate time series,…