Deep Unfolding with Approximated Computations for Rapid Optimization
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917332237942784 |
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| author | Avrahami, Dvir Milstein, Amit Chaux, Caroline Routtenberg, Tirza Shlezinger, Nir |
| author_facet | Avrahami, Dvir Milstein, Amit Chaux, Caroline Routtenberg, Tirza Shlezinger, Nir |
| contents | Optimization-based solvers play a central role in a wide range of signal processing and communication tasks. However, their applicability in latency-sensitive systems is limited by the sequential nature of iterative methods and the high computational cost per iteration. While deep unfolding has emerged as a powerful paradigm for converting iterative algorithms into learned models that operate with a fixed number of iterations, it does not inherently address the cost of each iteration. In this paper, we introduce a learned optimization framework that jointly tackles iteration count and per-iteration complexity. Our approach is based on unfolding a fixed number of optimization steps, replacing selected iterations with low-complexity approximated computations, and learning extended hyperparameters from data to compensate for the introduced approximations. We demonstrate the effectiveness of our method on two representative problems: (i) hybrid beamforming; and (ii) robust principal component analysis. These fundamental case studies show that our learned approximated optimizers can achieve state-of-the-art performance while reducing computational complexity by over three orders of magnitude. Our results highlight the potential of our approach to enable rapid, interpretable, and efficient decision-making in real-time systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00782 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Unfolding with Approximated Computations for Rapid Optimization Avrahami, Dvir Milstein, Amit Chaux, Caroline Routtenberg, Tirza Shlezinger, Nir Signal Processing Optimization-based solvers play a central role in a wide range of signal processing and communication tasks. However, their applicability in latency-sensitive systems is limited by the sequential nature of iterative methods and the high computational cost per iteration. While deep unfolding has emerged as a powerful paradigm for converting iterative algorithms into learned models that operate with a fixed number of iterations, it does not inherently address the cost of each iteration. In this paper, we introduce a learned optimization framework that jointly tackles iteration count and per-iteration complexity. Our approach is based on unfolding a fixed number of optimization steps, replacing selected iterations with low-complexity approximated computations, and learning extended hyperparameters from data to compensate for the introduced approximations. We demonstrate the effectiveness of our method on two representative problems: (i) hybrid beamforming; and (ii) robust principal component analysis. These fundamental case studies show that our learned approximated optimizers can achieve state-of-the-art performance while reducing computational complexity by over three orders of magnitude. Our results highlight the potential of our approach to enable rapid, interpretable, and efficient decision-making in real-time systems. |
| title | Deep Unfolding with Approximated Computations for Rapid Optimization |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.00782 |