The main development is currently performed in volesti, a generic open source C++ library, with R and Python interfaces. volesti implements various algorithms for highdimensional sampling and volume approximation as well as functions for copula estimation in financial modelling and metabolic network analysis.
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dingo is a python package that analyzes metabolic networks. It relies on high dimensional sampling with Markov Chain Monte Carlo (MCMC) methods and fast optimization methods to analyze the possible states of a metabolic network. To perform MCMC sampling, dingo relies on the C++ library volesti, which provides several algorithms for sampling convex polytopes. dingo also performs two standard methods to analyze the flux space of a metabolic network, namely Flux Balance Analysis and Flux Variability Analysis.
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Who is using our software ?

G. Spallitta, G. Masina, P. Morettin, A. Passerini, R. Sebastiani, Enhancing SMTbased Weighted Model Integration by Structure Awareness, Preprint, 2023. WWW

M. VejdemoJohansson, S. Mukherjee, Multiple hypothesis testing with persistent homology, American Institute of Mathematical Sciences, Volume 4, 2022. WWW

A. Venzke, D.K. Molzahn, S. Chatzivasileiadis  Efficient creation of datasets for datadriven power system applications, Electric Power Systems Research, Volume 190, 2021. WWW

P.Z.D. Martires, Samuel Kolb  Monte Carlo AntiDifferentiation for Approximate Weighted Model Integration, 2020. WWW

C. Maria, O. RouillĂ©  Computation of Large Asymptotics of 3Manifold Quantum Invariants, 2020. WWW
Google Summer of Code
GeomScale is participating in Google Summer of Code 2023 as a mentoring organization!
GeomScale was accepted and participated in Google Summer of Code 2020, Google Summer of Code 2021 and Google Summer of Code 2022 as a mentoring organization. The following projects was successfully completed:
 Memory allocation in facet redundancy removal in dingo (GSoC 2022).
 Sampling correlation matrices (GSoC 2022).
 Randomized SDP solver with Riemannian Hamiltonian Monte Carlo (GSoC 2022).
 Support for new sampling methods and new model formats in dingo (GSoC 2022).
 Counting linear extensions with volume computation (GSoC 2022).

Automatic differentiation support in volesti (GSoC 2022).
 From DNA sequences to metabolic interactions: building a pipeline to extract key metabolic processes (GSoC 2021).
 Monte Carlo Integration (GSoC 2021).
 Counting linear extensions (GSoC 2021).
 High dimensional geometric computations with least matrix inequalities (GSoC 2021).

Parallel Geometric Random Walks with Sparse Numerical Optimizations (GSoC 2021).
 A comparative study of uniform high dimensional samplers (GSoC 2020).

Sampling from HighDimensional logconcave densities (GSoC 2020).
 Optimization and Sum of Squares (GSoC 2020).
Members of GeomScale has successfully participated in GSoC in the past with the Rproject for statistical computing.