Algorithm Design

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Di Lu - One of the best experts on this subject based on the ideXlab platform.

  • Distributed Spectrum Sharing Algorithm Design and Realization
    2010 IEEE 71st Vehicular Technology Conference, 2010
    Co-Authors: Binyang Xu, Feng Yang, Di Lu
    Abstract:

    This paper studies Algorithm Design and realization for distributed spectrum sharing. In Algorithm Design, one game theory based solution, price-based distributed spectrum sharing (PDSS) Algorithm is presented to achieve optimal spectrum utilization. To realize the Algorithm, synchronous and sequential schemes are investigated for iterative execution of user spectrum sharing strategy. The synchronous and sequential PDSSs are studied and compared with those of iterative max SINR Algorithm in spectrum efficiency and power efficiency under various spectrum and user situations. Numerical results show the advantages of price theory over general game theory and sequential realization over synchronized realization in distributed spectrum sharing application.

  • VTC Spring - Distributed Spectrum Sharing Algorithm Design and Realization
    2010 IEEE 71st Vehicular Technology Conference, 2010
    Co-Authors: Binyang Xu, Feng Yang, Di Lu
    Abstract:

    This paper studies Algorithm Design and realization for distributed spectrum sharing. In Algorithm Design, one game theory based solution, price-based distributed spectrum sharing (PDSS) Algorithm is presented to achieve optimal spectrum utilization. To realize the Algorithm, synchronous and sequential schemes are investigated for iterative execution of user spectrum sharing strategy. The synchronous and sequential PDSSs are studied and compared with those of iterative max SINR Algorithm in spectrum efficiency and power efficiency under various spectrum and user situations. Numerical results show the advantages of price theory over general game theory and sequential realization over synchronized realization in distributed spectrum sharing application.

Binyang Xu - One of the best experts on this subject based on the ideXlab platform.

  • Distributed Spectrum Sharing Algorithm Design and Realization
    2010 IEEE 71st Vehicular Technology Conference, 2010
    Co-Authors: Binyang Xu, Feng Yang, Di Lu
    Abstract:

    This paper studies Algorithm Design and realization for distributed spectrum sharing. In Algorithm Design, one game theory based solution, price-based distributed spectrum sharing (PDSS) Algorithm is presented to achieve optimal spectrum utilization. To realize the Algorithm, synchronous and sequential schemes are investigated for iterative execution of user spectrum sharing strategy. The synchronous and sequential PDSSs are studied and compared with those of iterative max SINR Algorithm in spectrum efficiency and power efficiency under various spectrum and user situations. Numerical results show the advantages of price theory over general game theory and sequential realization over synchronized realization in distributed spectrum sharing application.

  • VTC Spring - Distributed Spectrum Sharing Algorithm Design and Realization
    2010 IEEE 71st Vehicular Technology Conference, 2010
    Co-Authors: Binyang Xu, Feng Yang, Di Lu
    Abstract:

    This paper studies Algorithm Design and realization for distributed spectrum sharing. In Algorithm Design, one game theory based solution, price-based distributed spectrum sharing (PDSS) Algorithm is presented to achieve optimal spectrum utilization. To realize the Algorithm, synchronous and sequential schemes are investigated for iterative execution of user spectrum sharing strategy. The synchronous and sequential PDSSs are studied and compared with those of iterative max SINR Algorithm in spectrum efficiency and power efficiency under various spectrum and user situations. Numerical results show the advantages of price theory over general game theory and sequential realization over synchronized realization in distributed spectrum sharing application.

Feng Yang - One of the best experts on this subject based on the ideXlab platform.

  • Distributed Spectrum Sharing Algorithm Design and Realization
    2010 IEEE 71st Vehicular Technology Conference, 2010
    Co-Authors: Binyang Xu, Feng Yang, Di Lu
    Abstract:

    This paper studies Algorithm Design and realization for distributed spectrum sharing. In Algorithm Design, one game theory based solution, price-based distributed spectrum sharing (PDSS) Algorithm is presented to achieve optimal spectrum utilization. To realize the Algorithm, synchronous and sequential schemes are investigated for iterative execution of user spectrum sharing strategy. The synchronous and sequential PDSSs are studied and compared with those of iterative max SINR Algorithm in spectrum efficiency and power efficiency under various spectrum and user situations. Numerical results show the advantages of price theory over general game theory and sequential realization over synchronized realization in distributed spectrum sharing application.

  • VTC Spring - Distributed Spectrum Sharing Algorithm Design and Realization
    2010 IEEE 71st Vehicular Technology Conference, 2010
    Co-Authors: Binyang Xu, Feng Yang, Di Lu
    Abstract:

    This paper studies Algorithm Design and realization for distributed spectrum sharing. In Algorithm Design, one game theory based solution, price-based distributed spectrum sharing (PDSS) Algorithm is presented to achieve optimal spectrum utilization. To realize the Algorithm, synchronous and sequential schemes are investigated for iterative execution of user spectrum sharing strategy. The synchronous and sequential PDSSs are studied and compared with those of iterative max SINR Algorithm in spectrum efficiency and power efficiency under various spectrum and user situations. Numerical results show the advantages of price theory over general game theory and sequential realization over synchronized realization in distributed spectrum sharing application.

Xiaopeng Li - One of the best experts on this subject based on the ideXlab platform.

  • Quantum adiabatic Algorithm Design using reinforcement learning
    Physical Review A, 2020
    Co-Authors: Xiaopeng Li
    Abstract:

    Quantum Algorithm Design plays a crucial role in exploiting the computational advantage of quantum devices. Here we develop a deep-reinforcement-learning based approach for quantum adiabatic Algorithm Design. Our approach is generically applicable to a class of problems with solution hard-to-find but easy-to-verify, e.g., searching and NP-complete problems. We benchmark this approach in Grover-search and 3-SAT problems, and find that the adiabatic-Algorithm obtained by our RL approach leads to significant improvement in the resultant success probability. In application to Grover search, our RL-Design automatically produces an adiabatic quantum Algorithm that has the quadratic speedup. We find for all our studied cases that quantitatively the RL-Designed Algorithm has a better performance compared to the analytically constructed non-linear Hamiltonian path when the encoding Hamiltonian is solvable, and that this RL-Design approach remains applicable even when the non-linear Hamiltonian path is not analytically available. In 3-SAT, we find RL-Design has fascinating transferability---the adiabatic Algorithm obtained by training on a specific choice of clause number leads to better performance consistently over the linear Algorithm on different clause numbers. These findings suggest the applicability of reinforcement learning for automated quantum adiabatic Algorithm Design. Further considering the established complexity-equivalence of circuit and adiabatic quantum Algorithms, we expect the RL-Designed adiabatic Algorithm to inspire novel circuit Algorithms as well. Our approach is potentially applicable to different quantum hardwares from trapped-ions and optical-lattices to superconducting-qubit devices.

  • Reinforcement learning architecture for automated quantum-adiabatic-Algorithm Design.
    2018
    Co-Authors: Xiaopeng Li
    Abstract:

    Quantum Algorithm Design lies in the hallmark of applications of quantum computation and quantum simulation. Here we put forward a deep reinforcement learning (RL) architecture for automated Algorithm Design in the framework of quantum adiabatic Algorithm, where the optimal Hamiltonian path to reach a quantum ground state that encodes a compution problem is obtained by RL techniques. We benchmark our approach in Grover search and 3-SAT problems, and find that the adiabatic Algorithm obtained by our RL approach leads to significant improvement in the success probability and computing speedups for both moderate and large number of qubits compared to conventional Algorithms. The RL-Designed Algorithm is found to be qualitatively distinct from the linear Algorithm in the resultant distribution of success probability. Considering the established complexity-equivalence of circuit and adiabatic quantum Algorithms, we expect the RL-Designed adiabatic Algorithm to inspire novel circuit Algorithms as well. Our approach offers a recipe to Design quantum Algorithms for generic problems through a machinery RL process, which paves a novel way to automated quantum Algorithm Design using artificial intelligence, potentially applicable to different quantum simulation and computation platforms from trapped ions and optical lattices to superconducting-qubit devices.

Matthew S Weinberg - One of the best experts on this subject based on the ideXlab platform.

  • understanding incentives mechanism Design becomes Algorithm Design
    Foundations of Computer Science, 2013
    Co-Authors: Constantinos Daskalakis, Matthew S Weinberg
    Abstract:

    We provide a computationally efficient black-box reduction from mechanism Design to Algorithm Design in very general settings. Specifically, we give an approximation-preserving reduction from truthfully maximizing any objective under arbitrary feasibility constraints with arbitrary bidder types to (not necessarily truthfully) maximizing the same objective plus virtual welfare (under the same feasibility constraints). Our reduction is based on a fundamentally new approach: we describe a mechanism's behavior indirectly only in terms of the expected value it awards bidders for certain behavior, and never directly access the allocation rule at all. Applying our new approach to revenue, we exhibit settings where our reduction holds both ways. That is, we also provide an approximation-sensitive reduction from (non-truthfully) maximizing virtual welfare to (truthfully) maximizing revenue, and therefore the two problems are computationally equivalent. With this equivalence in hand, we show that both problems are NP-hard to approximate within any polynomial factor, even for a single monotone sub modular bidder. We further demonstrate the applicability of our reduction by providing a truthful mechanism maximizing fractional max-min fairness.

  • understanding incentives mechanism Design becomes Algorithm Design
    arXiv: Computer Science and Game Theory, 2013
    Co-Authors: Constantinos Daskalakis, Matthew S Weinberg
    Abstract:

    We provide a computationally efficient black-box reduction from mechanism Design to Algorithm Design in very general settings. Specifically, we give an approximation-preserving reduction from truthfully maximizing \emph{any} objective under \emph{arbitrary} feasibility constraints with \emph{arbitrary} bidder types to (not necessarily truthfully) maximizing the same objective plus virtual welfare (under the same feasibility constraints). Our reduction is based on a fundamentally new approach: we describe a mechanism's behavior indirectly only in terms of the expected value it awards bidders for certain behavior, and never directly access the allocation rule at all. Applying our new approach to revenue, we exhibit settings where our reduction holds \emph{both ways}. That is, we also provide an approximation-sensitive reduction from (non-truthfully) maximizing virtual welfare to (truthfully) maximizing revenue, and therefore the two problems are computationally equivalent. With this equivalence in hand, we show that both problems are NP-hard to approximate within any polynomial factor, even for a single monotone submodular bidder. We further demonstrate the applicability of our reduction by providing a truthful mechanism maximizing fractional max-min fairness. This is the first instance of a truthful mechanism that optimizes a non-linear objective.