Owing to the manually fixed step size, the conventional gradient projection (GP) method requires relatively long time to solve the reconfigurable intelligent surface (RIS) aided hybrid beamforming problem. In order to...
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Owing to the manually fixed step size, the conventional gradient projection (GP) method requires relatively long time to solve the reconfigurable intelligent surface (RIS) aided hybrid beamforming problem. In order to speed up the GP method, we propose to learn the step sizes by using deep learning. Since the proposed deep learning architecture has a coordinate ascent structure, every step in the deep learning is explainable. Due to the simple multi-layer architecture, the proposed unrolled GP method has a strong out-of-distribution generalization capability. Under a single training setting, the unrolled GP approach is tested under thirty nine different out-of-distribution settings. The extensive simulation results show that the unrolled GP method has larger achievable rate than the GP method under middle-to-high signal-to-noise ratio (SNR) settings, and the proposed method is ten times faster than the GP method for all settings.
Black-Box Optimization (BBO) is increasingly vital for addressing complex real-world optimization challenges, where traditional methods fall short due to their reliance on expert knowledge and time-consuming processes...
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Black-Box Optimization (BBO) is increasingly vital for addressing complex real-world optimization challenges, where traditional methods fall short due to their reliance on expert knowledge and time-consuming processes. Meta-Black-Box Optimization (MetaBBO) emerges as a pivotal solution, leveraging meta-learning to enhance or discover optimization algorithms automatically. Originating from Automatic Algorithm Design (AAD), MetaBBO has branched into areas such as learn to optimize (L2O), Automated Design of Meta-heuristic Algorithm (ADMA), and Automatic Evolutionary Computation (AEC), each contributing to the advancement of the field. This comprehensive survey integrates and synthesizes the extant research within MetaBBO for Evolutionary Algorithms (EAs) to develop a consistent community of this research topic. Specifically, a mathematical model for MetaBBO is established, and its boundaries and scope are clarified. The potential optimization objects in MetaBBO for EAs is explored, providing insights into design space. A taxonomy of MetaBBO methodologies is introduced, reflecting the state-of-the-art from a meta-level perspective. Additionally, a comprehensive overview of benchmarks, evaluation metrics, and platforms is presented, streamlining the research process for those engaged in learning and experimentation in MetaBBO for EA. The survey concludes with an outlook on research, underscoring future directions and the pivotal role of MetaBBO in automatic algorithm design and optimization problem-solving.
Post-silicon validation is a crucial yet challenging problem primarily due to the increasing complexity of the semiconductor value chain. Existing techniques cannot keep up with the rapid increase in the complexity of...
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ISBN:
(纸本)9798350336344
Post-silicon validation is a crucial yet challenging problem primarily due to the increasing complexity of the semiconductor value chain. Existing techniques cannot keep up with the rapid increase in the complexity of designs. Therefore, post-silicon validation is becoming an expensive bottleneck. Robust performance tuning is relevant to compensate impacts of process variations and non-ideal design implementations. We propose a novel approach based on Deep Reinforcement learning and learn to optimize. The method automatically learns flexible tuning strategies tailored to specific circuits. Additionally, it addresses high-dimensional tuning tasks, including mixed data types and dependencies, e.g., on operating conditions. In this work, we introduce learn to Tune and demonstrate its appealing properties in post-silicon validation, e.g., lower computational cost or faster time-to-optimize, allowing a more efficient adaption of the tuning to changing tuning conditions than classical methods.
Towards the global endeavor of clean energy transition, there is a rapid development of distributed energy resources installed in the premises of residential or commercial users, enabling them to act as flexible energ...
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Towards the global endeavor of clean energy transition, there is a rapid development of distributed energy resources installed in the premises of residential or commercial users, enabling them to act as flexible energy prosumers. Empowering prosumers is envisioned as a catalytic development for modern energy economies, with recent research, as well as innovation and policy actions, pointing to the promising direction of decentralized energy markets, where active energy prosumers exchange energy in a decentralized fashion. Despite the vast amount of recent research on prosumer-centric peer-to-peer (p2p) energy markets, only a small subset of studies accounts for managing the inherent uncertainty of prosumers' flexible *** this paper, we consider the problem of controlling the decisions of energy prosumers' within a p2p exchange network. The multi-bilateral economic dispatch is formulated as an optimal control problem. The proposed solution is based on a direct lookahead policy, effectively addressing the issues of dimensionality and local constraint satisfaction. Experimental simulations demonstrate the method's efficiency and the system's behavior. The proposed formulation and method is shown to effectively address the operation of p2p markets under uncertainty, closely tracking the performance of the (full information) optimal-in-hindsight benchmark.
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