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‎404.html‎

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<h2>Latest</h2>
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<li><a href="/publication/admmslack/">Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size</a></li>
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<li><a href="/publication/gato/">GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control</a></li>
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<li><a href="/publication/conictinympc/">Code Generation and Conic Constraints for Model-Predictive Control on Microcontrollers with Conic-TinyMPC</a></li>
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<li><a href="/publication/fpgatinympc/">MPC Solver Hardware Generation Framework with Model-Specific Operation Fusion and Pruning</a></li>
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<li><a href="/publication/admmslack/">Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size</a></li>
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<b> We are hiring for a postdoctoral researcher to start as soon as April 2026 for our project with <a href="https://www.tri.global/">Toyota Research Institute</a> on real-time, uncertainty-aware optimal control, targeting autonomous driving at the limits of handling, validated through real-world deployments! <a href="/triuniversity3postdocadd/">Learn more and apply here!</a> </b>
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<meta property="og:description" content="The A²R Lab at Barnard College, Columbia University, focusses on developing and implementing open-source algorithms for dynamic motion planning and control of robots by exploiting both the mathematical structure of algorithms and the design of computational platforms. As such, our research is at the intersection of Robotics and Computer Architecture, Embedded Systems, Numerical Optimization, and Machine Learning. We also want to improve the accessibility of STEM education. We therefore undertake research to understand and improve diversity, equity, inclusion, and belonging in STEM education globally and explore ways to design new interdisciplinary, project-based, open-access courses that lower the barrier to entry of cutting edge topics like robotics and embedded machine learning."><meta property="og:image" content="https://a2r-lab.github.io/img/icon-192.png">
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<a href="/publication/admmslack/">Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size</a>
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<a href="/publication/gato/">GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control</a>
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<a href="/publication/fpgatinympc/">MPC Solver Hardware Generation Framework with Model-Specific Operation Fusion and Pruning</a>
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<a href="/publication/admmslack/">Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size</a>
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<a href="/publication/hjcdik/">HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent</a>
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‎authors/brianplancher/index.xml‎

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<copyright>&amp;copy; {year} Brian Plancher</copyright>
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<title>Solving Quadratic Programs with Slack Variables via ADMM without Increasing the Problem Size</title>
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<link>https://a2r-lab.github.io/publication/admmslack/</link>
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<description>We propose a simple ADMM scheme to tackle QPs with slack variables without increasing the size of the original problem. The only modification is a slightly different projection in the z-update, while the rest of the algorithm remains standard. We prove that the method is equivalent to applying ADMM to the QP with additional slack variables, even though slack variables are not added. Numerical experiments show speedups of the approach.</description>
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<description>We developed a flexible MPC solver hardware generation framework which includes a parameterized and programmable vector architecture template that accommodates instruction-level and data-level parallelism in vector and matrix functional units, and a model-specific fused architecture. Implementation of the proposed processor on the Ultra96 platform achieves up to a 9.73x speedup compared to existing generic solutions on MCUs. Moreover, end-to-end performance tests reveal that this speedup reduces the overall control error by 25.96%.</description>
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<description>We propose a simple ADMM scheme to tackle QPs with slack variables without increasing the size of the original problem. The only modification is a slightly different projection in the z-update, while the rest of the algorithm remains standard. We prove that the method is equivalent to applying ADMM to the QP with additional slack variables, even though slack variables are not added. Numerical experiments show speedups of the approach.</description>
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‎authors/index.html‎

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<meta property="og:description" content="The A²R Lab at Barnard College, Columbia University, focusses on developing and implementing open-source algorithms for dynamic motion planning and control of robots by exploiting both the mathematical structure of algorithms and the design of computational platforms. As such, our research is at the intersection of Robotics and Computer Architecture, Embedded Systems, Numerical Optimization, and Machine Learning. We also want to improve the accessibility of STEM education. We therefore undertake research to understand and improve diversity, equity, inclusion, and belonging in STEM education globally and explore ways to design new interdisciplinary, project-based, open-access courses that lower the barrier to entry of cutting edge topics like robotics and embedded machine learning."><meta property="og:image" content="https://a2r-lab.github.io/img/icon-192.png">
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<li><a href="https://a2r-lab.github.io/authors/alexdu/">Alex Du</a></li>
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<li><a href="https://a2r-lab.github.io/authors/brianplancher/">Brian Plancher</a></li>
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<li><a href="https://a2r-lab.github.io/authors/brianplancher/">Brian Plancher</a></li>
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<li><a href="https://a2r-lab.github.io/authors/johnsubosits/">John Subosits</a></li>
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<li><a href="https://a2r-lab.github.io/authors/marcusgreiff/">Marcus Greiff</a></li>
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<li><a href="https://a2r-lab.github.io/authors/thomaslew/">Thomas Lew</a></li>
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<li><a href="https://a2r-lab.github.io/authors/alexdu/">Alex Du</a></li>
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<li><a href="https://a2r-lab.github.io/authors/emreadabag/">Emre Adabag</a></li>
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<li><a href="https://a2r-lab.github.io/authors/shuosha/">Shuo &#34;Chris&#34; Sha</a></li>
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<li><a href="https://a2r-lab.github.io/authors/benjaminbrown/">Benjamin Brown</a></li>
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<li><a href="https://a2r-lab.github.io/authors/emanuelmoss/">Emanuel Moss</a></li>
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<li><a href="https://a2r-lab.github.io/authors/jacobhuckelberry/">Jacob Huckelberry</a></li>
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‎authors/index.xml‎

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<meta property="og:description" content="The A²R Lab at Barnard College, Columbia University, focusses on developing and implementing open-source algorithms for dynamic motion planning and control of robots by exploiting both the mathematical structure of algorithms and the design of computational platforms. As such, our research is at the intersection of Robotics and Computer Architecture, Embedded Systems, Numerical Optimization, and Machine Learning. We also want to improve the accessibility of STEM education. We therefore undertake research to understand and improve diversity, equity, inclusion, and belonging in STEM education globally and explore ways to design new interdisciplinary, project-based, open-access courses that lower the barrier to entry of cutting edge topics like robotics and embedded machine learning."><meta property="og:image" content="https://a2r-lab.github.io/img/icon-192.png">
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<description>We propose a simple ADMM scheme to tackle QPs with slack variables without increasing the size of the original problem. The only modification is a slightly different projection in the z-update, while the rest of the algorithm remains standard. We prove that the method is equivalent to applying ADMM to the QP with additional slack variables, even though slack variables are not added. Numerical experiments show speedups of the approach.</description>
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