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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.02573 |
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| _version_ | 1866929682821152768 |
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| author | Boyd, Stephen P. Parshakova, Tetiana Ryu, Ernest K. Suh, Jaewook J. |
| author_facet | Boyd, Stephen P. Parshakova, Tetiana Ryu, Ernest K. Suh, Jaewook J. |
| contents | We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits. Given an optimization problem, the first stage of the methodology is to design an appropriate electric circuit whose continuous-time dynamics converge to the solution of the optimization problem at hand. Then, the second stage is an automated, computer-assisted discretization of the continuous-time dynamics, yielding a provably convergent discrete-time algorithm. Our methodology recovers many classical (distributed) optimization algorithms and enables users to quickly design and explore a wide range of new algorithms with convergence guarantees. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02573 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimization Algorithm Design via Electric Circuits Boyd, Stephen P. Parshakova, Tetiana Ryu, Ernest K. Suh, Jaewook J. Optimization and Control Machine Learning 47H05, 90C25, 37M15 We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits. Given an optimization problem, the first stage of the methodology is to design an appropriate electric circuit whose continuous-time dynamics converge to the solution of the optimization problem at hand. Then, the second stage is an automated, computer-assisted discretization of the continuous-time dynamics, yielding a provably convergent discrete-time algorithm. Our methodology recovers many classical (distributed) optimization algorithms and enables users to quickly design and explore a wide range of new algorithms with convergence guarantees. |
| title | Optimization Algorithm Design via Electric Circuits |
| topic | Optimization and Control Machine Learning 47H05, 90C25, 37M15 |
| url | https://arxiv.org/abs/2411.02573 |