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线性计算编码:指数搜索与降维状态算法

Linear Computation Coding: Exponential Search and Reduced-State Algorithms

Hans Rosenberger, Johanna S. Fröhlich, Ali Bereyhi, Ralf R. Müller

AI总结:

本文研究线性计算编码,提出逐步最优搜索算法以超越DMP,并针对大矩阵引入降维状态算法,在保持计算可行性的同时实现至少10%的性能提升。

摘要:

线性计算编码关注多维线性函数的压缩,即降低将任意向量乘以任意已知常数矩阵的计算量。本文通过逐步最优搜索改进了基于离散匹配追踪(DMP)算法的现有技术。尽管其性能显著优于DMP,但对于大型矩阵和高精度要求在计算上不可行。因此,引入了一种降维状态算法,其性能优于DMP,且即使对于大型矩阵在计算上仍然可行。根据矩阵大小,相比DMP的性能提升至少在10%量级。

英文摘要:

Linear computation coding is concerned with the compression of multidimensional linear functions, i.e. with reducing the computational effort of multiplying an arbitrary vector to an arbitrary, but known, constant matrix. This paper advances over the state-of-the art, that is based on a discrete matching pursuit (DMP) algorithm, by a step-wise optimal search. Offering significant performance gains over DMP, it is however computationally infeasible for large matrices and high accuracy. Therefore, a reduced-state algorithm is introduced that offers performance superior to DMP, while still being computationally feasible even for large matrices. Depending on the matrix size, the performance gain over DMP is on the order of at least 10%.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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