11-magic : Recovery of sparse signals via convex programming
11-magic : Recovery of sparse signals via convex programming
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发表时间：
2005
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通讯作者：
E. Candès;J. Romberg
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作者：
E. Candès;J. Romberg
为了最大限度地提高计算效率，七个问题的求解器分别实现。然而，它们都具有相同的基本结构，计算瓶颈是牛顿步的计算（下面将详细讨论）。该代码既可以用于“小规模”模式，即系统被明确构造并精确求解，也可以用于“大规模”模式，即使用共轭梯度（CG）等迭代无矩阵算法近似求解系统。
For maximum computational efficiency, the solvers for each of the seven problems are implemented separately. They all have the same basic structure, however, with the computational bottleneck being the calculation of the Newton step (this is discussed in detail below). The code can be used in either “small scale” mode, where the system is constructed explicitly and solved exactly, or in “large scale” mode, where an iterative matrix-free algorithm such as conjugate gradients (CG) is used to approximately solve the system.