CVXPY

Thanks @AGIHouseSF for the awesome autoresearch hackathon! We explored accelerating CVXPY with autoresearch github.com/SteveDiamond/c… and hacked with @traviscline on unleashing the Apple Neural Engine github.com/tmc/autoresear… Shoutout to @hyperbolic_labs for the free compute and @ensue_ai for the autoresearch harness!

Thanks again to all those who attended our first ever CVXPY Workshop! Workshop talks are now live on YouTube: youtube.com/playlist?list=…

We loved seeing the creative Hackathon projects! Thanks again to the participants! github.com/cvxpy/hackathon

Thanks everyone for attending our first every CVXPY Workshop and Hackathon! We will be sharing the talks on the CVXPY Youtube channel: @cvxpy" target="_blank" rel="nofollow noopener">youtube.com/@cvxpy

Sign up link docs.google.com/forms/d/e/1FAI…

We're excited to announce CVXPY 1.8: We've added a new COO backend that greatly improves performance on problems with lots of parameters; we've added HiGHS as our new MILP solver, added support for an approx flag on atoms that are historically approximated as SOCs, but can be evaluated exactly with power cones, and new atoms: logical boolean operations for your MILPs and einsum and stack! Also, our workshop is Feb 20 and 21! We're still looking for speakers, and we want you! Please sign up to present and share how you're using CVXPY. Talks can be as short as five minutes!

🎉Attend or speak at the inaugural CVXPY Community Workshop on February 20 and 21st at Stanford University and on Zoom! Register here: docs.google.com/forms/d/e/1FAI… 📅Additionally, the CVXPY developers are hosting listening sessions to learn more about what you are using CVXPY for and how we can better serve you. Register here: calendar.app.google/mRGBur1ZVMW1dc…

Topics will cover: * GPU-enabled large-scale optimization using randomized linear algebra * GPU-accelerated Quadratic Conic Solvers * An Overview of GPU-Based First-Order Methods for Linear Programming and Beyond * GPU Implementation of Second-Order Linear and Nonlinear Programming Solvers * GPU-based Split algorithm for Large-Scale CVRPSD * Nonlinear Optimization with GPU-Accelerated Neural Network Constraints * Learning to optimize over linearly convergent algorithms: gotta characterize 'em all * On meta prompt optimization and coding agents * Accelerating Parametric Convex Optimization: Design and Verification of First-Order Methods * Automation in Optimization: Enhancing Decomposition for Proximal and CPU/GPU-Parallel Methods Plus there will be a special CVXPY announcement! cvxgrp.org/scaleopt/

Interested in GPU-accelerating optimization and doing optimization at scale? Attending NeurIPS? Check out the ScaleOPT tutorial on December 7 and hear from speakers such as Madeleine Udell, Brandon Amos, Wotao Yin, Haihao Lu, Bartolomeo Stellato, and Danny Tse!

CVXPY 1.7 adds support for GPU solvers, multiple attributes, more N-dimensional atoms, and many other exciting features!

Thanks everyone for attending our session at #iccopt2025!

🌟 CVXPY just surpassed 5,000 stars on GitHub! 🌟 Thanks to all the contributors and users who made this possible! 🔗 CVXPY on GitHub lnkd.in/d7pazBGp 📢 Join our Discord community to stay connected lnkd.in/dpEQA7mT

CVXPY 1.3 is out! github.com/cvxpy/cvxpy/re… It includes faster problem compilation, Python 3.11 support, and much more!

Another great CVXPY session at #iccopt2022!

Thanks everyone for attending the #CVXPY tutorial at #SciPy2022!

You can try CVXPYgen today --- it's on PyPI (pip install cvxpygen). Check out the docs for more info github.com/cvxgrp/cvxpygen

CVXPYgen also lets you import your custom solver as a Python module

CVXPY now supports code generation, via the CVXPYgen package by @maxschaller8611: code: github.com/cvxgrp/cvxpygen paper: web.stanford.edu/~boyd/papers/c… CVXPYgen lets you solve parametrized CVXPY problems anywhere you can run C programs --- like on a drone.