[{"data":1,"prerenderedAt":453},["ShallowReactive",2],{"project:sage-acsv":3,"catalog":42},{"slug":4,"name":5,"nameHtml":5,"summary":6,"summaryHtml":6,"authors":7,"searchText":19,"facets":20,"source":35,"docs":37,"descriptionHtml":38,"bibliographyHtml":39,"funding":40,"affiliations":41},"sage-acsv","sage_acsv","SageMath algorithms for computing asymptotics of multivariate sequences with rational generating functions, using exact algebraic computations.",[8,11,13,15,17],{"name":9,"orcid":10,"html":9},"Benjamin Hackl","https:\u002F\u002Forcid.org\u002F0000-0003-2998-9599",{"name":12,"html":12},"Andrew Luo",{"name":14,"html":14},"Stephen Melczer",{"name":16,"html":16},"Jesse Selover",{"name":18,"html":18},"Elaine Wong","sage_acsv sagemath algorithms for computing asymptotics of multivariate sequences with rational generating functions, using exact algebraic computations. benjamin hackl andrew luo stephen melczer jesse selover elaine wong sage_acsv implements algorithms for analytic combinatorics in several variables in sagemath. it computes asymptotic information about multivariate sequences with rational generating functions, using exact algebraic computations. for generating functions of the form f(\\mathbf{z}) = \\frac{g(\\mathbf{z})}{h(\\mathbf{z})}, the geometry of the singular variety h(\\mathbf{z})=0 is central to the asymptotic analysis. the package's original algorithms treat smooth cases; subsequent work extends the implementation to non-smooth singular varieties under additional geometric assumptions, using whitney stratification. further capabilities include higher-order asymptotic expansions and alternative backends for algebraic computations. documentation and examples are available online, and the package can be explored through binder .",{"tags":21,"audiences":25,"affiliations":27,"license":28,"maintenance":30,"resources":32},[22,23,24],"analytic combinatorics","asymptotic analysis","symbolic computation",[26],"research",[],[29],"MIT",[31],"active",[33,34],"documentation","publications",{"url":36},"https:\u002F\u002Fgithub.com\u002FACSVMath\u002Fsage_acsv","https:\u002F\u002Facsvmath.github.io\u002Fsage_acsv\u002F","\u003Cp>\u003Ccode>sage_acsv\u003C\u002Fcode> implements algorithms for analytic combinatorics in several variables\nin SageMath. 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748 318 748C317 748 314 747 311 745C251 699 201 631 160 540C121 453 101 371 101 292L101 208C101 129 121 47 160-40C201-131 251-199 311-245C314-247 317-248 318-248Z\">\u003C\u002Fpath>\u003C\u002Fg>\u003Cg data-mml-node=\"TeXAtom\" data-latex=\"\\mathbf{z}\" data-mjx-texclass=\"ORD\" transform=\"translate(1270,0)\">\u003Cg data-mml-node=\"mi\" data-latex=\"z\">\u003Cpath data-c=\"1D433\" d=\"M462 202L415 202C406 101 390 40 258 40L172 40L449 401C457 412 459 414 459 423C459 444 445 444 427 444L60 444L48 269L95 269C101 372 132 408 235 408L320 408L41 44C32 33 32 31 32 22C32 0 45 0 64 0L444 0Z\">\u003C\u002Fpath>\u003C\u002Fg>\u003C\u002Fg>\u003Cg data-mml-node=\"mo\" data-latex=\")\" transform=\"translate(1781,0)\">\u003Cpath data-c=\"29\" d=\"M78-245C138-199 188-131 229-40C268 47 288 129 288 208L288 292C288 371 268 453 229 540C188 631 138 699 78 745C75 747 72 748 71 748C62 748 57 743 57 734C57 730 59 726 62 723C114 683 156 617 187 526C214 447 228 369 228 292L228 208C228 131 214 53 187-26C156-117 114-183 62-223C59-227 57-231 57-234C57-243 62-248 71-248C72-248 75-247 78-245Z\">\u003C\u002Fpath>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003Cmjx-break size=\"4\"> \u003C\u002Fmjx-break>\u003Csvg style=\"vertical-align: -0.566ex;\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" width=\"3.52ex\" height=\"2.262ex\" role=\"img\" focusable=\"false\" viewBox=\"0 -750 1555.8 1000\">\u003Cg stroke=\"currentColor\" fill=\"currentColor\" stroke-width=\"0\" transform=\"scale(1,-1)\">\u003Cg data-mml-node=\"math\" data-latex=\"H(\\mathbf{z})=0\">\u003Cg data-mml-node=\"mo\" data-latex=\"=\">\u003Cpath data-c=\"3D\" d=\"M698 367L80 367C64 367 56 359 56 344C56 329 64 321 80 321L698 321C714 321 722 329 722 344C722 356 711 367 698 367M698 179L80 179C64 179 56 171 56 156C56 141 64 133 80 133L698 133C714 133 722 141 722 156C722 169 711 179 698 179Z\">\u003C\u002Fpath>\u003C\u002Fg>\u003Cg data-mml-node=\"mn\" data-latex=\"0\" transform=\"translate(1055.8,0)\">\u003Cpath data-c=\"30\" d=\"M249-22C390-22 460 92 460 320C460 473 428 575 365 625C330 652 291 666 250 666C109 666 39 551 39 320C39 136 88-22 249-22M361 524C368 489 371 425 371 332C371 240 367 172 360 128C347 48 310 8 249 8C226 8 203 17 182 34C155 57 139 104 132 176C129 201 128 253 128 332C128 419 131 480 136 513C145 568 163 603 191 618C213 630 232 636 249 636C314 636 350 583 361 524Z\">\u003C\u002Fpath>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003C\u002Fmjx-container> is central to the\nasymptotic analysis. The package's original algorithms treat smooth cases;\nsubsequent work extends the implementation to non-smooth singular varieties\nunder additional geometric assumptions, using Whitney stratification.\u003C\u002Fp>\n\u003Cp>Further capabilities include higher-order asymptotic expansions and alternative\nbackends for algebraic computations. Documentation and examples are available\nonline, and the package can be explored through\n\u003Ca href=\"https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002FACSVMath\u002Fsage_acsv\u002FHEAD\">Binder\u003C\u002Fa>.\u003C\u002Fp>","\u003Cul class=\"\">\n\u003Cli class=\"\">Hackl, B., Luo, A., Melczer, S., Selover, J., & Wong, E. (2023). \u003Ca href=\"https:\u002F\u002Fwww.mat.univie.ac.at\u002F~slc\u002Fwpapers\u002FFPSAC2023\u002F90.html\">Rigorous Analytic Combinatorics in Several Variables in SageMath\u003C\u002Fa>. \u003Ci>Séminaire Lotharingien de Combinatoire\u003C\u002Fi>, \u003Ci>89B\u003C\u002Fi>(90), 1–12. \u003Ca href=\"https:\u002F\u002Fwww.mat.univie.ac.at\u002F~slc\u002Fwpapers\u002FFPSAC2023\u002F90.html\">https:\u002F\u002Fwww.mat.univie.ac.at\u002F~slc\u002Fwpapers\u002FFPSAC2023\u002F90.html\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli class=\"\">Hackl, B., Luo, A., Melczer, S., & Schost, É. (2025). \u003Ca href=\"https:\u002F\u002Fwww.mat.univie.ac.at\u002F~slc\u002Fwpapers\u002FFPSAC2025\u002F159.html\">A SageMath Package for Analytic Combinatorics in Several Variables: Beyond the Smooth Case\u003C\u002Fa>. \u003Ci>Séminaire Lotharingien de Combinatoire\u003C\u002Fi>, \u003Ci>93B\u003C\u002Fi>(159), 1–12. \u003Ca href=\"https:\u002F\u002Fwww.mat.univie.ac.at\u002F~slc\u002Fwpapers\u002FFPSAC2025\u002F159.html\">https:\u002F\u002Fwww.mat.univie.ac.at\u002F~slc\u002Fwpapers\u002FFPSAC2025\u002F159.html\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>",[],[],{"version":43,"projects":44,"facets":396},1,[45,73,90,104,125,143,158,180,199,215,234,249,263,282,303,321,336,353,383],{"slug":46,"name":47,"nameHtml":47,"summary":48,"summaryHtml":48,"authors":49,"searchText":60,"facets":61},"avionic","AVIONIC","GPU-accelerated variational reconstruction of undersampled dynamic MRI data, including estimation of receiver-coil sensitivities.",[50,52,55,57],{"name":51,"html":51},"Andreas Schwarzl",{"name":53,"orcid":54,"html":53},"Martin Holler","https:\u002F\u002Forcid.org\u002F0000-0002-2895-2375",{"name":56,"html":56},"Matthias Schloegl",{"name":58,"orcid":59,"html":58},"Kristian Bredies","https:\u002F\u002Forcid.org\u002F0000-0001-7140-043X","avionic gpu-accelerated variational reconstruction of undersampled dynamic mri data, including estimation of receiver-coil sensitivities. andreas schwarzl martin holler matthias schloegl kristian bredies avionic — accelerated variational dynamic mri reconstruction implements gpu-accelerated reconstruction of highly undersampled dynamic magnetic resonance measurements. applications include cardiac imaging and dynamic contrast-enhanced mri. the software supports cartesian and non-cartesian sampling when the required trajectory information is available. it also includes variational estimation of receiver-coil sensitivity profiles and reconstruction methods based on infimal convolution of total generalized variation functionals. the primary repository is hosted on tu graz gitlab. the older github repository is archived and points to that location; the mirror's archival status does not imply that development of the software has ended. the readme and bundled license file give different gpl\u002Flgpl notices. consult the repository's licensing information before reuse.",{"tags":62,"audiences":67,"affiliations":68,"license":69,"maintenance":71,"resources":72},[63,64,65,66],"inverse problems","mathematical imaging","magnetic resonance imaging","GPU computing",[26],[],[70],"unspecified",[70],[33,34],{"slug":74,"name":75,"nameHtml":75,"summary":76,"summaryHtml":76,"authors":77,"searchText":80,"facets":81},"cellsim","CellSim","Julia simulation code for cell mechanics in confined environments, with configurable cortex, nucleus, and confinement models.",[78],{"name":79,"html":79},"Gaspard Jankowiak","cellsim julia simulation code for cell mechanics in confined environments, with configurable cortex, nucleus, and confinement models. gaspard jankowiak cellsim is a julia codebase for numerical simulations of cell mechanics. its modules represent the cell cortex, nucleus, centrosome, and confining walls, with utilities for plotting and measuring simulation results. yaml configuration files control parameters such as membrane elasticity, cortex viscosity, polymerization speed, confinement geometry, and nuclear mechanics. the repository includes configurations with and without a nucleus, parameter sweeps, and scripts for collecting and plotting metrics. this is a research simulation codebase rather than a registered julia package. the readme provides environment setup and execution instructions; users should check its dependency requirements before running the examples.",{"tags":82,"audiences":85,"affiliations":86,"license":87,"maintenance":88,"resources":89},[83,84],"mathematical biology","numerical simulation",[26],[],[70],[70],[33],{"slug":91,"name":91,"nameHtml":91,"summary":92,"summaryHtml":92,"authors":93,"searchText":95,"facets":96},"combpyter","A lightweight Python library for enumerating Dyck paths and studying their combinatorial statistics.",[94],{"name":9,"orcid":10,"html":9},"combpyter a lightweight python library for enumerating dyck paths and studying their combinatorial statistics. benjamin hackl combpyter is a lightweight python library for generating and analysing combinatorial objects. it is a personal, research-driven project with a focused scope: the documented implementation currently supports dyck paths through the dyckpath and dyckpaths interfaces. the readme demonstrates enumerating dyck paths of semilength 8 and computing the distribution of their numbers of peaks. this statistic is described by the narayana numbers n(n,k) = \\frac{1}{n}\\binom{n}{k}\\binom{n}{k-1}. the package is installable from pypi and includes tests in its source repository.",{"tags":97,"audiences":99,"affiliations":100,"license":101,"maintenance":102,"resources":103},[98],"enumerative combinatorics",[26],[],[29],[70],[33],{"slug":105,"name":106,"nameHtml":106,"summary":107,"summaryHtml":107,"authors":108,"searchText":115,"facets":116},"coupled-tgv-recon","coupled_tgv_recon","MATLAB algorithms for coupled multichannel image reconstruction, including joint magnetic resonance and positron emission tomography reconstruction.",[109,111,112,113],{"name":110,"html":110},"Florian Knoll",{"name":53,"orcid":54,"html":53},{"name":58,"orcid":59,"html":58},{"name":114,"html":114},"Thomas Koesters","coupled_tgv_recon matlab algorithms for coupled multichannel image reconstruction, including joint magnetic resonance and positron emission tomography reconstruction. florian knoll martin holler kristian bredies thomas koesters coupled_tgv_recon provides matlab code for multichannel regularized image reconstruction using coupled total generalized variation. applications include multi-contrast denoising and joint reconstruction of magnetic resonance (mr) and positron emission tomography (pet) measurements. the repository contains a reconstruction algorithm, a control script, and example data and operators. its examples range from a digital brain phantom to an in-vivo mr–pet experiment, for which additional data must be downloaded. the pet examples use emrecon projection operators; some are supplied as compiled matlab extensions. the implementation supports different coupling norms and is designed to be extended to additional datasets and forward operators. the readme documents operator normalization, reconstruction parameters, and platform-specific requirements. the full in-vivo example has substantial memory and runtime requirements.",{"tags":117,"audiences":119,"affiliations":120,"license":121,"maintenance":123,"resources":124},[63,64,65,118],"tomography",[26],[],[122],"GPL-3.0",[70],[33],{"slug":126,"name":127,"nameHtml":127,"summary":128,"summaryHtml":128,"authors":129,"searchText":131,"facets":132},"cyclic-group-zerosum-factor","Exhaustive Search for Minimal Factorizations of Zero-Sum Sequences in Cyclic Groups","A multithreaded Rust program for exhaustive searches for factorizations into four minimal zero-sum sequences in cyclic groups of even order.",[130],{"name":9,"orcid":10,"html":9},"exhaustive search for minimal factorizations of zero-sum sequences in cyclic groups a multithreaded rust program for exhaustive searches for factorizations into four minimal zero-sum sequences in cyclic groups of even order. benjamin hackl this rust program, distributed as cyclic-group-zerosum-factor , carries out exhaustive computational experiments on zero-sum sequences in cyclic groups of even order c_{2n} . for selected generators a,b,c , it investigates whether a sequence of the form u = a^{2n}b^{2n}c^{2n} = u_1u_2u_3u_4 admits a factorization into four non-empty minimal zero-sum sequences. candidate factorizations are enumerated through products of weak integer compositions, with batches of candidates distributed to worker threads. the zenodo archive includes the rust source, cargo metadata, and a readme with the mathematical formulation, implementation notes, and experimental results. the command cargo run --release -- \u003Cn> selects the group order 2n . this is a standalone program, separate from the zero-sum-sequences python package. the license recorded for the deposited software is cc by 4.0.",{"tags":133,"audiences":137,"affiliations":138,"license":139,"maintenance":141,"resources":142},[134,135,136],"additive combinatorics","factorization theory","exhaustive search",[26],[],[140],"CC-BY-4.0",[70],[],{"slug":144,"name":145,"nameHtml":145,"summary":146,"summaryHtml":146,"authors":147,"searchText":149,"facets":150},"dependent-bterms","dependent_bterms","SageMath tools for asymptotic expansions with explicit error bounds and a secondary variable whose growth depends on the main variable.",[148],{"name":9,"orcid":10,"html":9},"dependent_bterms sagemath tools for asymptotic expansions with explicit error bounds and a secondary variable whose growth depends on the main variable. benjamin hackl dependent_bterms extends sagemath's asymptoticring framework with a secondary symbolic variable whose growth is bounded by powers of the main asymptotic variable. this supports computations in which, for example, 1 \\leq k \\leq n^{1\u002F2} while n tends to infinity. the toolbox combines asymptotic expansions with explicit error bounds. it provides operations for simplifying expansions, rounding error-bound coefficients, changing the range of validity of bounds, and constructing taylor expansions with explicit remainders. a demonstration notebook introduces the package's capabilities. the individual functions include sagemath examples in their docstrings.",{"tags":151,"audiences":152,"affiliations":153,"license":154,"maintenance":156,"resources":157},[23,24],[26],[],[155],"GPL-3.0-or-later",[31],[33],{"slug":159,"name":160,"nameHtml":160,"summary":161,"summaryHtml":161,"authors":162,"searchText":170,"facets":171},"gpdps","GPDPS.jl","A Julia reference implementation of generalized primal-dual proximal splitting for nonsmooth, nonconvex optimization.",[163,166,168],{"name":164,"orcid":165,"html":164},"Christian Clason","https:\u002F\u002Forcid.org\u002F0000-0002-9948-8426",{"name":167,"html":167},"Stanislav Mazurenko",{"name":169,"html":169},"Tuomo Valkonen","gpdps.jl a julia reference implementation of generalized primal-dual proximal splitting for nonsmooth, nonconvex optimization. christian clason stanislav mazurenko tuomo valkonen gpdps.jl provides the reference implementation of a generalized primal-dual proximal splitting approach based on generalized conjugation. it accompanies research on nonsmooth, nonconvex optimization. the package includes examples for an elliptic nash equilibrium problem and a huber–potts image segmentation model. the latter supports isotropic and anisotropic variants and configurable images and regularization parameters. the documented test environment uses julia 1.0–1.2 on macos and linux. the repository supplies a project environment and instructions for instantiating its dependencies; compatibility with newer julia versions should be checked before use.",{"tags":172,"audiences":175,"affiliations":176,"license":177,"maintenance":178,"resources":179},[173,174,64],"numerical optimization","nonsmooth optimization",[26],[],[29],[70],[33,34],{"slug":181,"name":182,"nameHtml":182,"summary":183,"summaryHtml":183,"authors":184,"searchText":190,"facets":191},"graptor","Graptor","GPU-accelerated tomographic reconstruction with variational regularization, including joint reconstruction of complementary electron microscopy channels.",[185,188,189],{"name":186,"orcid":187,"html":186},"Richard Huber","https:\u002F\u002Forcid.org\u002F0000-0003-1743-6786",{"name":53,"orcid":54,"html":53},{"name":58,"orcid":59,"html":58},"graptor gpu-accelerated tomographic reconstruction with variational regularization, including joint reconstruction of complementary electron microscopy channels. richard huber martin holler kristian bredies graptor — graz application for tomographic reconstruction reconstructs images from radon-transform data using iterative variational methods and multi-channel total generalized variation regularization. a particular focus is joint reconstruction of complementary channels, originally haadf and edx measurements in scanning transmission electron tomography. the opencl\u002Fgpu implementation is accompanied by a graphical user interface, preprocessing options, and a command-line reconstruction script. the project originated in a collaboration between mathematics at the university of graz and electron microscopy and nanoanalysis at graz university of technology. the repository includes phantom data and commands for reproducing the numerical results of the associated publication.",{"tags":192,"audiences":194,"affiliations":195,"license":196,"maintenance":197,"resources":198},[118,63,193,66],"numerical analysis",[26],[],[155],[70],[33,34],{"slug":200,"name":201,"nameHtml":201,"summary":202,"summaryHtml":202,"authors":203,"searchText":207,"facets":208},"gratopy","Gratopy","OpenCL-accelerated Radon and fanbeam projections, backprojections, and iterative tomographic reconstruction methods for Python.",[204,205,206],{"name":58,"orcid":59,"html":58},{"name":186,"orcid":187,"html":186},{"name":9,"orcid":10,"html":9},"gratopy opencl-accelerated radon and fanbeam projections, backprojections, and iterative tomographic reconstruction methods for python. kristian bredies richard huber benjamin hackl gratopy — graz accelerated tomographic projections for python provides radon transforms, fanbeam transforms, and their associated backprojections. its pixel-driven projection operators are implemented with opencl for efficient gpu execution and integration with other pyopencl code. the toolbox is intended for developing iterative tomographic reconstruction methods, including optimization-based approaches. it includes landweber, conjugate-gradient, and total-variation reconstruction schemes, together with examples and test data. an experimental radon operator interface supports adjoints and compositions such as r^\\ast r . fanbeam execution remains available through the established projection interface. opencl drivers and a suitable pyopencl installation are required.",{"tags":209,"audiences":210,"affiliations":211,"license":212,"maintenance":213,"resources":214},[118,63,193,66],[26],[],[155],[31],[33,34],{"slug":216,"name":217,"nameHtml":217,"summary":218,"summaryHtml":218,"authors":219,"authorsNote":221,"authorsNoteHtml":221,"searchText":222,"facets":223},"manim","Manim","A community-developed Python library for creating precise mathematical animations programmatically, for explanatory videos, teaching, and presentations.",[220],{"name":9,"orcid":10,"html":9},"and the Manim community","manim a community-developed python library for creating precise mathematical animations programmatically, for explanatory videos, teaching, and presentations. benjamin hackl and the manim community manim community is a python library for creating mathematical animations programmatically. scenes are defined in python and rendered into videos, making it possible to build precise, reproducible visual explanations for teaching, presentations, and science communication. this entry refers to the community edition, which was forked from the animation tool originally created by grant sanderson for 3blue1brown. it is developed by an international community; the author list here highlights a department member rather than attempting to list all contributors. manim can be used from the command line or within jupyter notebooks. the documentation includes installation instructions, tutorials, and an example gallery. an online jupyter environment lets users try it without a local installation.",{"tags":224,"audiences":227,"affiliations":230,"license":231,"maintenance":232,"resources":233},[225,226],"mathematical visualization","animation",[228,229],"teaching","science communication",[],[29],[31],[33],{"slug":235,"name":235,"nameHtml":235,"summary":236,"summaryHtml":236,"authors":237,"searchText":240,"facets":241},"nlpdegm","MATLAB reference implementations of primal-dual extragradient methods for nonlinear, nonsmooth PDE-constrained optimization.",[238,239],{"name":164,"orcid":165,"html":164},{"name":169,"html":169},"nlpdegm matlab reference implementations of primal-dual extragradient methods for nonlinear, nonsmooth pde-constrained optimization. christian clason tuomo valkonen nlpdegm provides matlab reference implementations for primal-dual extragradient methods applied to nonlinear, nonsmooth pde-constrained optimization. it accompanies the research article by christian clason and tuomo valkonen. the repository contains finite-element setup code and implementations for l^1 fitting, l^\\infty fitting, and state-constrained problems. it is paper-specific research code rather than a general-purpose optimization package.",{"tags":242,"audiences":244,"affiliations":245,"license":246,"maintenance":247,"resources":248},[174,243,173],"optimal control",[26],[],[70],[70],[33,34],{"slug":4,"name":5,"nameHtml":5,"summary":6,"summaryHtml":6,"authors":250,"searchText":19,"facets":256},[251,252,253,254,255],{"name":9,"orcid":10,"html":9},{"name":12,"html":12},{"name":14,"html":14},{"name":16,"html":16},{"name":18,"html":18},{"tags":257,"audiences":258,"affiliations":259,"license":260,"maintenance":261,"resources":262},[22,23,24],[26],[],[29],[31],[33,34],{"slug":264,"name":265,"nameHtml":265,"summary":266,"summaryHtml":266,"authors":267,"searchText":273,"facets":274},"sagemath-asymptotic-ring","SageMath Asymptotic Ring","Symbolic arithmetic with univariate and multivariate asymptotic expansions, including polynomial, logarithmic, and exponential growth.",[268,269,271],{"name":9,"orcid":10,"html":9},{"name":270,"html":270},"Daniel Krenn",{"name":272,"html":272},"Clemens Heuberger","sagemath asymptotic ring symbolic arithmetic with univariate and multivariate asymptotic expansions, including polynomial, logarithmic, and exponential growth. benjamin hackl daniel krenn clemens heuberger sagemath's asymptotic ring provides a framework for symbolic asymptotic expansions. it combines growth groups with coefficient rings and supports arithmetic with exact terms and asymptotic error terms in one or several variables. the framework handles polynomial, logarithmic, and exponential growth and provides operations such as multiplication, powers, logarithms, and exponentials of expansions. the sagemath reference manual contains extensive worked examples and describes the available growth groups and term types. this entry concerns the asymptotic-expansion component of sagemath, not the whole computer algebra system. it is distributed as part of sagemath and is distinct from the separate dependent_bterms extension for explicit error bounds with a dependent secondary variable.",{"tags":275,"audiences":276,"affiliations":277,"license":278,"maintenance":280,"resources":281},[23,24],[26],[],[279],"GPL-2.0-or-later",[31],[33],{"slug":283,"name":284,"nameHtml":284,"summary":285,"summaryHtml":285,"authors":286,"searchText":293,"facets":294},"sagemath-banff-cluster-algebras-and-finite-laurent-intersection-rings","Banff cluster algebras and finite Laurent intersection rings in SageMath","SageMath classes for finite Laurent intersection rings (FLIRs) and Banff cluster algebras, with algorithms for membership testing, divisor\u002Fclass group computation, and factorization.",[287,290],{"name":288,"orcid":289,"html":288},"Mara Pompili","https:\u002F\u002Forcid.org\u002F0000-0003-0681-7241",{"name":291,"orcid":292,"html":291},"Daniel Smertnig","https:\u002F\u002Forcid.org\u002F0000-0002-5391-2471","banff cluster algebras and finite laurent intersection rings in sagemath sagemath classes for finite laurent intersection rings (flirs) and banff cluster algebras, with algorithms for membership testing, divisor\u002Fclass group computation, and factorization. mara pompili daniel smertnig this sagemath extension adds two new algebra classes: finitelaurentintersectionring — a finite intersection of laurent polynomial rings together with birational change-of-charts data, providing divisor groups, prime divisors, and class group computations. banffclusteralgebra — a banff (locally acyclic) cluster algebra realized both as a cluster algebra and as a finite laurent intersection ring, giving effective algorithms for membership, divisor groups, class groups, and factorization. these implement the algorithms of pompili and smertnig (2026) , who introduced flirs as a class of rings that contains locally acyclic cluster algebras, full-rank upper cluster algebras, and several other families. the class-group and factoriality algorithms use multivariate polynomial factorization and avoid expensive grobner basis calculations. the code is being added to sagemath via pr #42538 (work in progress) and is licensed under the gnu gpl v2 or later, in line with the rest of sagemath.",{"tags":295,"audiences":298,"affiliations":299,"license":300,"maintenance":301,"resources":302},[296,297,135,24],"cluster algebras","commutative algebra",[26],[],[279],[31],[34],{"slug":304,"name":305,"nameHtml":305,"summary":306,"summaryHtml":306,"authors":307,"searchText":312,"facets":313},"solvopt","SolvOpt","A historical solver for local nonlinear and nonsmooth optimization, distributed with MATLAB, C, and Fortran implementations.",[308,310],{"name":309,"html":309},"Alexei Kuntsevich",{"name":311,"html":311},"Franz Kappel","solvopt a historical solver for local nonlinear and nonsmooth optimization, distributed with matlab, c, and fortran implementations. alexei kuntsevich franz kappel solvopt — solver for local nonlinear optimization problems is a historical university of graz optimization package by alexei kuntsevich and franz kappel. it uses a modified version of shor's r-algorithm for local optimization of nonlinear, potentially nonsmooth functions and supports constrained problems. the department's download site preserves version 1.1, released in june 1997, with matlab, c, and fortran source distributions, test problems, demonstration code, and a detailed manual. this entry records that historical distribution and is marked archived under the directory's pre-2015 update policy. the original installation instructions target much older compiler and matlab environments. compatibility with current systems is not implied. the manual describes the software as freeware; consult its original terms before reuse.",{"tags":314,"audiences":315,"affiliations":316,"license":317,"maintenance":318,"resources":320},[174,173],[26],[],[70],[319],"archived",[33],{"slug":322,"name":323,"nameHtml":323,"summary":324,"summaryHtml":324,"authors":325,"searchText":327,"facets":328},"tgv-pycuda","tgv_pycuda","GPU-accelerated TV and TGV methods for denoising, deblurring, upscaling, dequantization, and compressive imaging in Python.",[326],{"name":58,"orcid":59,"html":58},"tgv_pycuda gpu-accelerated tv and tgv methods for denoising, deblurring, upscaling, dequantization, and compressive imaging in python. kristian bredies tgv_pycuda implements primal-dual algorithms for imaging problems regularized by total variation (tv) and second-order total generalized variation (tgv). the python implementation uses pycuda for gpu acceleration and requires a cuda-capable gpu and a working cuda installation. the repository includes algorithms, tests, and examples for denoising, deblurring, zooming, dequantization, and compressive imaging. a guided jupyter notebook reproduces figures and numerical experiments from the associated publication on recovering piecewise smooth multichannel images.",{"tags":329,"audiences":330,"affiliations":331,"license":332,"maintenance":334,"resources":335},[63,64,173,66],[26],[],[333],"Apache-2.0",[70],[33,34],{"slug":337,"name":337,"nameHtml":337,"summary":338,"summaryHtml":338,"authors":339,"searchText":345,"facets":346},"vectormultibang","MATLAB implementations of convex relaxation for discrete vector-valued optimization, with examples in optimal control and branched transport.",[340,341,343],{"name":164,"orcid":165,"html":164},{"name":342,"html":342},"Carla Tameling",{"name":344,"html":344},"Benedikt Wirth","vectormultibang matlab implementations of convex relaxation for discrete vector-valued optimization, with examples in optimal control and branched transport. christian clason carla tameling benedikt wirth vectormultibang contains matlab implementations accompanying research on convex relaxation of discrete vector-valued optimization problems. these methods address settings in which admissible controls or material choices are restricted to discrete vectors. three groups of examples cover optimal control of the bloch equation, optimal control of linearized elasticity, and multimaterial branched transport. each has a dedicated test script in the repository.",{"tags":347,"audiences":348,"affiliations":349,"license":350,"maintenance":351,"resources":352},[174,243,173],[26],[],[29],[70],[33,34],{"slug":354,"name":354,"nameHtml":354,"summary":355,"summaryHtml":355,"authors":356,"searchText":371,"facets":372},"vitabel","A Python framework for loading, aligning, visualizing, and interactively annotating physiological time series in Jupyter notebooks.",[357,360,363,364,367,370],{"name":358,"orcid":359,"html":358},"Simon Orlob","https:\u002F\u002Forcid.org\u002F0000-0001-7799-4822",{"name":361,"orcid":362,"html":361},"Wolfgang J. Kern","https:\u002F\u002Forcid.org\u002F0000-0001-5080-382X",{"name":9,"orcid":10,"html":9},{"name":365,"orcid":366,"html":365},"Jan Wnent","https:\u002F\u002Forcid.org\u002F0000-0002-8685-858X",{"name":368,"orcid":369,"html":368},"Jan-Thorsten Gräsner","https:\u002F\u002Forcid.org\u002F0000-0001-8143-0376",{"name":53,"orcid":54,"html":53},"vitabel a python framework for loading, aligning, visualizing, and interactively annotating physiological time series in jupyter notebooks. simon orlob wolfgang j. kern benjamin hackl jan wnent jan-thorsten grasner martin holler vitabel is a python framework for loading, visualizing, aligning, and annotating high-resolution physiological time series in jupyter notebooks. it supports retrospective critical-care and perioperative research workflows involving recordings from defibrillators, anaesthesia systems, and patient monitors. its central vitals container combines channels, labels, and metadata. users can align recordings from multiple sources, add derived labels, and curate data for signal processing, statistics, and machine learning. supported workflows include loading defibrillator recordings and vitaldb data, computing ventilation-related signals, and interactively annotating events. the project provides documentation, worked examples, and an interactive binder demonstration .",{"tags":373,"audiences":378,"affiliations":379,"license":380,"maintenance":381,"resources":382},[374,375,376,377],"medical data","time series","data annotation","machine learning",[26],[],[29],[31],[33,34],{"slug":384,"name":384,"nameHtml":384,"summary":385,"summaryHtml":385,"authors":386,"searchText":388,"facets":389},"zero-sum-sequences","Python tools for factoring finite additive sequences into minimal zero-sum sequences and exploring factorization lengths, relations, and automorphism orbits.",[387],{"name":9,"orcid":10,"html":9},"zero-sum-sequences python tools for factoring finite additive sequences into minimal zero-sum sequences and exploring factorization lengths, relations, and automorphism orbits. benjamin hackl zero-sum-sequences provides immutable finite additive sequences and algorithms for factoring them into minimal zero-sum sequences. it runs in ordinary python with networkx and optionally integrates with additive groups supplied by sagemath. for a sequence over an additive group g , the zero-sum condition is g_1 + \\cdots + g_m = 0. the package computes sets of factorization lengths, minimum and maximum lengths, witnesses, and complete factorizations. it also supports atom catalogues, factorization relations, and automorphism orbits. the ambient group and an upper bound for its davenport constant are supplied explicitly by the caller. for example, over c_3 , the minimal zero-sum sequences a=1^3 , b=2^3 , and p=1\\cdot 2 satisfy the factorization relation ab=p^3 . an executable tutorial introduces the api with small examples; the repository also includes benchmarks.",{"tags":390,"audiences":391,"affiliations":392,"license":393,"maintenance":394,"resources":395},[134,135,24],[26],[],[29],[31],[33],{"tags":397,"audiences":428,"affiliations":433,"license":434,"maintenance":443,"resources":449},[398,400,401,402,404,405,406,407,408,409,410,412,414,415,416,417,418,419,420,421,422,423,424,425,426,427],{"value":134,"label":134,"html":134,"count":399},2,{"value":22,"label":22,"html":22,"count":43},{"value":226,"label":226,"html":226,"count":43},{"value":23,"label":23,"html":23,"count":403},3,{"value":296,"label":296,"html":296,"count":43},{"value":297,"label":297,"html":297,"count":43},{"value":376,"label":376,"html":376,"count":43},{"value":98,"label":98,"html":98,"count":43},{"value":136,"label":136,"html":136,"count":43},{"value":135,"label":135,"html":135,"count":403},{"value":66,"label":66,"html":66,"count":411},4,{"value":63,"label":63,"html":63,"count":413},5,{"value":377,"label":377,"html":377,"count":43},{"value":65,"label":65,"html":65,"count":399},{"value":83,"label":83,"html":83,"count":43},{"value":64,"label":64,"html":64,"count":411},{"value":225,"label":225,"html":225,"count":43},{"value":374,"label":374,"html":374,"count":43},{"value":174,"label":174,"html":174,"count":411},{"value":193,"label":193,"html":193,"count":399},{"value":173,"label":173,"html":173,"count":413},{"value":84,"label":84,"html":84,"count":43},{"value":243,"label":243,"html":243,"count":399},{"value":24,"label":24,"html":24,"count":413},{"value":375,"label":375,"html":375,"count":43},{"value":118,"label":118,"html":118,"count":403},[429,431,432],{"value":26,"label":26,"html":26,"count":430},18,{"value":229,"label":229,"html":229,"count":43},{"value":228,"label":228,"html":228,"count":43},[],[435,436,437,438,439,440,442],{"value":333,"label":333,"html":333,"count":43},{"value":140,"label":140,"html":140,"count":43},{"value":279,"label":279,"html":279,"count":399},{"value":122,"label":122,"html":122,"count":43},{"value":155,"label":155,"html":155,"count":403},{"value":29,"label":29,"html":29,"count":441},7,{"value":70,"label":70,"html":70,"count":411},[444,446,447],{"value":31,"label":31,"html":31,"count":445},8,{"value":319,"label":319,"html":319,"count":43},{"value":70,"label":70,"html":70,"count":448},10,[450,452],{"value":33,"label":33,"html":33,"count":451},17,{"value":34,"label":34,"html":34,"count":448},1789745190579]