[{"data":1,"prerenderedAt":458},["ShallowReactive",2],{"project:gratopy":3,"catalog":48},{"slug":4,"name":5,"nameHtml":5,"summary":6,"summaryHtml":6,"authors":7,"searchText":17,"facets":18,"source":34,"docs":37,"descriptionHtml":38,"bibliographyHtml":39,"funding":40,"affiliations":47},"gratopy","Gratopy","OpenCL-accelerated Radon and fanbeam projections, backprojections, and iterative tomographic reconstruction methods for Python.",[8,11,14],{"name":9,"orcid":10,"html":9},"Kristian Bredies","https:\u002F\u002Forcid.org\u002F0000-0001-7140-043X",{"name":12,"orcid":13,"html":12},"Richard Huber","https:\u002F\u002Forcid.org\u002F0000-0003-1743-6786",{"name":15,"orcid":16,"html":15},"Benjamin Hackl","https:\u002F\u002Forcid.org\u002F0000-0003-2998-9599","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":19,"audiences":24,"affiliations":26,"license":27,"maintenance":29,"resources":31},[20,21,22,23],"tomography","inverse problems","numerical analysis","GPU computing",[25],"research",[],[28],"GPL-3.0-or-later",[30],"active",[32,33],"documentation","publications",{"url":35,"doi":36},"https:\u002F\u002Fgithub.com\u002Fkbredies\u002Fgratopy","10.5281\u002Fzenodo.5221442","https:\u002F\u002Fgratopy.readthedocs.io\u002F","\u003Cp>\u003Cstrong>Gratopy — Graz accelerated tomographic projections for Python\u003C\u002Fstrong> provides\nRadon transforms, fanbeam transforms, and their associated backprojections.\nIts pixel-driven projection operators are implemented with OpenCL for efficient\nGPU execution and integration with other PyOpenCL code.\u003C\u002Fp>\n\u003Cp>The toolbox is intended for developing iterative tomographic reconstruction\nmethods, including optimization-based approaches. It includes Landweber,\nconjugate-gradient, and total-variation reconstruction schemes, together with\nexamples and test data.\u003C\u002Fp>\n\u003Cp>An experimental Radon operator interface supports adjoints and compositions\nsuch as \u003Cmjx-container class=\"MathJax\" jax=\"SVG\" overflow=\"overflow\">\u003Csvg aria-label=\"R^\\ast R\" style=\"vertical-align: -0.05ex;\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" width=\"4.422ex\" height=\"1.61ex\" role=\"img\" focusable=\"false\" viewBox=\"0 -689.7 1954.6 711.7\">\u003Cg stroke=\"currentColor\" fill=\"currentColor\" stroke-width=\"0\" transform=\"scale(1,-1)\">\u003Cg data-mml-node=\"math\" data-latex=\"R^\\ast R\">\u003Cg data-mml-node=\"msup\" data-latex=\"R^\\ast\">\u003Cg data-mml-node=\"mi\" data-latex=\"R\">\u003Cpath data-c=\"1D445\" d=\"M739 531C739 582 713 621 662 649C621 672 572 683 517 683L235 683C212 683 202 682 202 659C202 652 205 647 211 646C221 645 229 644 234 644C264 643 281 641 286 640C291 639 294 636 294 631C294 629 293 623 290 613L158 82C153 60 143 46 128 41C121 39 103 38 72 38C50 38 41 37 41 15C41 4 47-1 59 0L183 3L309 0C325-1 333 8 333 23C333 33 322 38 301 38C261 38 241 43 241 52C241 52 242 54 244 68L308 327L423 327C492 327 527 298 527 241C527 232 522 210 513 174C502 132 497 104 497 89C497 13 556-22 632-22C660-22 687-9 714 16C741 41 755 68 755 96C755 106 750 111 739 111C732 111 726 106 723 95C712 64 698 41 682 28C666 15 651 8 636 8C613 8 601 27 601 64C601 88 604 125 611 176C614 197 615 212 615 223C615 276 587 315 531 339C625 362 739 429 739 531M609 616C629 603 639 581 639 550C639 530 635 507 626 480C599 398 531 357 422 357L316 357L379 610C384 631 392 642 403 643C408 644 428 644 463 644C528 644 566 642 609 616Z\">\u003C\u002Fpath>\u003C\u002Fg>\u003Cg data-mml-node=\"mo\" transform=\"translate(792,363) scale(0.707)\" data-latex=\"\\ast\">\u003Cpath data-c=\"2217\" d=\"M404 372C397 372 390 370 385 366L267 279L282 425C285 445 269 462 250 462C231 462 215 445 218 425L233 279L115 366C110 370 103 372 96 372C78 372 63 357 63 339C63 325 70 315 83 309L217 250L83 191C70 185 63 175 63 161C63 143 78 128 96 128C103 128 110 130 115 134L233 221L218 75C215 55 231 39 250 39C269 39 285 55 282 75L267 221L385 134C390 130 397 128 404 128C415 128 424 133 431 142C446 162 435 183 417 191L283 250L417 309C430 315 437 325 437 339C437 357 422 372 404 372Z\">\u003C\u002Fpath>\u003C\u002Fg>\u003C\u002Fg>\u003Cg data-mml-node=\"mi\" data-latex=\"R\" transform=\"translate(1195.6,0)\">\u003Cpath data-c=\"1D445\" d=\"M739 531C739 582 713 621 662 649C621 672 572 683 517 683L235 683C212 683 202 682 202 659C202 652 205 647 211 646C221 645 229 644 234 644C264 643 281 641 286 640C291 639 294 636 294 631C294 629 293 623 290 613L158 82C153 60 143 46 128 41C121 39 103 38 72 38C50 38 41 37 41 15C41 4 47-1 59 0L183 3L309 0C325-1 333 8 333 23C333 33 322 38 301 38C261 38 241 43 241 52C241 52 242 54 244 68L308 327L423 327C492 327 527 298 527 241C527 232 522 210 513 174C502 132 497 104 497 89C497 13 556-22 632-22C660-22 687-9 714 16C741 41 755 68 755 96C755 106 750 111 739 111C732 111 726 106 723 95C712 64 698 41 682 28C666 15 651 8 636 8C613 8 601 27 601 64C601 88 604 125 611 176C614 197 615 212 615 223C615 276 587 315 531 339C625 362 739 429 739 531M609 616C629 603 639 581 639 550C639 530 635 507 626 480C599 398 531 357 422 357L316 357L379 610C384 631 392 642 403 643C408 644 428 644 463 644C528 644 566 642 609 616Z\">\u003C\u002Fpath>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fsvg>\u003C\u002Fmjx-container>. Fanbeam execution remains available through the established\nprojection interface. OpenCL drivers and a suitable PyOpenCL installation are\nrequired.\u003C\u002Fp>","\u003Cul class=\"\">\n\u003Cli class=\"\">Bredies, K., & Huber, R. (2021). \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1137\u002F20M1326635\">Convergence Analysis of Pixel-Driven Radon and Fanbeam Transforms\u003C\u002Fa>. \u003Ci>SIAM Journal on Numerical Analysis\u003C\u002Fi>, \u003Ci>59\u003C\u002Fi>(3), 1399–1432. \u003Ca href=\"https:\u002F\u002Fdoi.org\u002F10.1137\u002F20M1326635\">https:\u002F\u002Fdoi.org\u002F10.1137\u002F20M1326635\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>",[41,44],{"funderHtml":42,"grantHtml":43},"Austrian Science Fund (FWF)","P-29192 — Regularization Graphs for Variational Imaging",{"funderHtml":45,"grantHtml":46},"German Research Foundation (DFG) and Austrian Science Fund (FWF)","IGDK 1754 \u002F W-1244 — Optimization and Numerical Analysis for Partial Differential Equations with Nonsmooth Structures",[],{"version":49,"projects":50,"facets":401},1,[51,75,92,107,127,145,161,183,199,211,230,245,268,287,308,326,341,358,388],{"slug":52,"name":53,"nameHtml":53,"summary":54,"summaryHtml":54,"authors":55,"searchText":64,"facets":65},"avionic","AVIONIC","GPU-accelerated variational reconstruction of undersampled dynamic MRI data, including estimation of receiver-coil sensitivities.",[56,58,61,63],{"name":57,"html":57},"Andreas Schwarzl",{"name":59,"orcid":60,"html":59},"Martin Holler","https:\u002F\u002Forcid.org\u002F0000-0002-2895-2375",{"name":62,"html":62},"Matthias Schloegl",{"name":9,"orcid":10,"html":9},"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":66,"audiences":69,"affiliations":70,"license":71,"maintenance":73,"resources":74},[21,67,68,23],"mathematical imaging","magnetic resonance 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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":84,"audiences":87,"affiliations":88,"license":89,"maintenance":90,"resources":91},[85,86],"mathematical biology","numerical simulation",[25],[],[72],[72],[32],{"slug":93,"name":93,"nameHtml":93,"summary":94,"summaryHtml":94,"authors":95,"searchText":97,"facets":98},"combpyter","A lightweight Python library for enumerating Dyck paths and studying their combinatorial statistics.",[96],{"name":15,"orcid":16,"html":15},"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":99,"audiences":101,"affiliations":102,"license":103,"maintenance":105,"resources":106},[100],"enumerative combinatorics",[25],[],[104],"MIT",[72],[32],{"slug":108,"name":109,"nameHtml":109,"summary":110,"summaryHtml":110,"authors":111,"searchText":118,"facets":119},"coupled-tgv-recon","coupled_tgv_recon","MATLAB algorithms for coupled multichannel image reconstruction, including joint magnetic resonance and positron emission tomography reconstruction.",[112,114,115,116],{"name":113,"html":113},"Florian Knoll",{"name":59,"orcid":60,"html":59},{"name":9,"orcid":10,"html":9},{"name":117,"html":117},"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":120,"audiences":121,"affiliations":122,"license":123,"maintenance":125,"resources":126},[21,67,68,20],[25],[],[124],"GPL-3.0",[72],[32],{"slug":128,"name":129,"nameHtml":129,"summary":130,"summaryHtml":130,"authors":131,"searchText":133,"facets":134},"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.",[132],{"name":15,"orcid":16,"html":15},"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":135,"audiences":139,"affiliations":140,"license":141,"maintenance":143,"resources":144},[136,137,138],"additive combinatorics","factorization theory","exhaustive search",[25],[],[142],"CC-BY-4.0",[72],[],{"slug":146,"name":147,"nameHtml":147,"summary":148,"summaryHtml":148,"authors":149,"searchText":151,"facets":152},"dependent-bterms","dependent_bterms","SageMath tools for asymptotic expansions with explicit error bounds and a secondary variable whose growth depends on the main variable.",[150],{"name":15,"orcid":16,"html":15},"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":153,"audiences":156,"affiliations":157,"license":158,"maintenance":159,"resources":160},[154,155],"asymptotic analysis","symbolic computation",[25],[],[28],[30],[32],{"slug":162,"name":163,"nameHtml":163,"summary":164,"summaryHtml":164,"authors":165,"searchText":173,"facets":174},"gpdps","GPDPS.jl","A Julia reference implementation of generalized primal-dual proximal splitting for nonsmooth, nonconvex optimization.",[166,169,171],{"name":167,"orcid":168,"html":167},"Christian Clason","https:\u002F\u002Forcid.org\u002F0000-0002-9948-8426",{"name":170,"html":170},"Stanislav Mazurenko",{"name":172,"html":172},"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":175,"audiences":178,"affiliations":179,"license":180,"maintenance":181,"resources":182},[176,177,67],"numerical optimization","nonsmooth optimization",[25],[],[104],[72],[32,33],{"slug":184,"name":185,"nameHtml":185,"summary":186,"summaryHtml":186,"authors":187,"searchText":191,"facets":192},"graptor","Graptor","GPU-accelerated tomographic reconstruction with variational regularization, including joint reconstruction of complementary electron microscopy channels.",[188,189,190],{"name":12,"orcid":13,"html":12},{"name":59,"orcid":60,"html":59},{"name":9,"orcid":10,"html":9},"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":193,"audiences":194,"affiliations":195,"license":196,"maintenance":197,"resources":198},[20,21,22,23],[25],[],[28],[72],[32,33],{"slug":4,"name":5,"nameHtml":5,"summary":6,"summaryHtml":6,"authors":200,"searchText":17,"facets":204},[201,202,203],{"name":9,"orcid":10,"html":9},{"name":12,"orcid":13,"html":12},{"name":15,"orcid":16,"html":15},{"tags":205,"audiences":206,"affiliations":207,"license":208,"maintenance":209,"resources":210},[20,21,22,23],[25],[],[28],[30],[32,33],{"slug":212,"name":213,"nameHtml":213,"summary":214,"summaryHtml":214,"authors":215,"authorsNote":217,"authorsNoteHtml":217,"searchText":218,"facets":219},"manim","Manim","A community-developed Python library for creating precise mathematical animations programmatically, for explanatory videos, teaching, and presentations.",[216],{"name":15,"orcid":16,"html":15},"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":220,"audiences":223,"affiliations":226,"license":227,"maintenance":228,"resources":229},[221,222],"mathematical visualization","animation",[224,225],"teaching","science communication",[],[104],[30],[32],{"slug":231,"name":231,"nameHtml":231,"summary":232,"summaryHtml":232,"authors":233,"searchText":236,"facets":237},"nlpdegm","MATLAB reference implementations of primal-dual extragradient methods for nonlinear, nonsmooth PDE-constrained optimization.",[234,235],{"name":167,"orcid":168,"html":167},{"name":172,"html":172},"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":238,"audiences":240,"affiliations":241,"license":242,"maintenance":243,"resources":244},[177,239,176],"optimal control",[25],[],[72],[72],[32,33],{"slug":246,"name":247,"nameHtml":247,"summary":248,"summaryHtml":248,"authors":249,"searchText":259,"facets":260},"sage-acsv","sage_acsv","SageMath algorithms for computing asymptotics of multivariate sequences with rational generating functions, using exact algebraic computations.",[250,251,253,255,257],{"name":15,"orcid":16,"html":15},{"name":252,"html":252},"Andrew Luo",{"name":254,"html":254},"Stephen Melczer",{"name":256,"html":256},"Jesse Selover",{"name":258,"html":258},"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":261,"audiences":263,"affiliations":264,"license":265,"maintenance":266,"resources":267},[262,154,155],"analytic combinatorics",[25],[],[104],[30],[32,33],{"slug":269,"name":270,"nameHtml":270,"summary":271,"summaryHtml":271,"authors":272,"searchText":278,"facets":279},"sagemath-asymptotic-ring","SageMath Asymptotic Ring","Symbolic arithmetic with univariate and multivariate asymptotic expansions, including polynomial, logarithmic, and exponential growth.",[273,274,276],{"name":15,"orcid":16,"html":15},{"name":275,"html":275},"Daniel Krenn",{"name":277,"html":277},"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":280,"audiences":281,"affiliations":282,"license":283,"maintenance":285,"resources":286},[154,155],[25],[],[284],"GPL-2.0-or-later",[30],[32],{"slug":288,"name":289,"nameHtml":289,"summary":290,"summaryHtml":290,"authors":291,"searchText":298,"facets":299},"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.",[292,295],{"name":293,"orcid":294,"html":293},"Mara Pompili","https:\u002F\u002Forcid.org\u002F0000-0003-0681-7241",{"name":296,"orcid":297,"html":296},"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":300,"audiences":303,"affiliations":304,"license":305,"maintenance":306,"resources":307},[301,302,137,155],"cluster algebras","commutative algebra",[25],[],[284],[30],[33],{"slug":309,"name":310,"nameHtml":310,"summary":311,"summaryHtml":311,"authors":312,"searchText":317,"facets":318},"solvopt","SolvOpt","A historical solver for local nonlinear and nonsmooth optimization, distributed with 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compatibility with current systems is not implied. the manual describes the software as freeware; consult its original terms before reuse.",{"tags":319,"audiences":320,"affiliations":321,"license":322,"maintenance":323,"resources":325},[177,176],[25],[],[72],[324],"archived",[32],{"slug":327,"name":328,"nameHtml":328,"summary":329,"summaryHtml":329,"authors":330,"searchText":332,"facets":333},"tgv-pycuda","tgv_pycuda","GPU-accelerated TV and TGV methods for denoising, deblurring, upscaling, dequantization, and compressive imaging in Python.",[331],{"name":9,"orcid":10,"html":9},"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 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