Mathematics Colloquia and Seminars

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Global Virtual Sage Days 112.358

Special Events

Speaker: Jean-Philippe Labbé, Blaec Bejarano, Harald Schilly, Hal Snyder, William Stein, Steven Diamond, Julian Hall, Nina Miolane, Vincent Delecroix, Jonathan Kliem, Matthias Köppe
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Location: Zoom
Start time: Wed, Jun 1 2022, 7:45AM

An online event on open-source mathematical software with the SageMath users and developers around the globe

Session 1: Introduction

Wednesday June 1, 07:45–08:45 Pacific. Session chair: Jean-Philippe Labbé

Session 2: Fresh Numerics from Upstream

Wednesday June 1, 09:00–11:45 Pacific. Session chair: Matthias Köppe

  • Steven Diamond: CVXPY. CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY has been used in hundreds of research projects and by Fortune 500 companies. In this talk, we will show how easy it is to formulate and solve optimization problems with CVXPY through example applications in finance and renewable energy. We will also highlight exciting recent work building on CVXPY.
  • Julian Hall: HiGHS: The world's best open-source linear optimization software—coming to SageMath! HiGHS is open-source linear optimization software developed by Hall and Edinburgh PhD students over the past six years. It solves large-scale sparse linear programming (LP) problems (using simplex and interior point methods), mixed-integer programming (MIP) problems and quadratic programming (QP) problems. Its overall performance on Mittelmann's benchmarks places it clearly ahead of any other open-source linear optimization software. This talk will introduce some of the techniques underpinning HiGHS, the environment in which it can be used, and its performance. In the world of software interfaces, HiGHS provides the LP and MIP solvers in the SciPy system, and is the open-source solver of choice in the popular modern Julia-based modelling and optimization system JuMP. The prospects for its integration into SageMath will be discussed.
  • Nina Miolane: Geomstats—a Python package for differential geometry in statistics and machine learning. We introduce Geomstats, an open-source Python package for computations and statistics on nonlinear manifolds that appear in machine learning applications, such as: hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Manifolds come equipped with families of Riemannian metrics with associated exponential and logarithmic maps, geodesics, and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering, and dimension reduction on manifolds. All associated operations provide support for different execution backends—namely NumPy, Autograd, PyTorch, and TensorFlow. This talk presents the package, compares it with related libraries, and provides relevant examples. We show that Geomstats provides reliable building blocks to both foster research in differential geometry and statistics and democratize the use of (Riemannian) geometry in statistics and machine learning. The source code is freely available under the MIT license at

Session 3: Sage Modularization and Packaging Summit

Wednesday June 1, 12:00–16:59 Pacific