Signal Reconstruction of Compressed Sensing Based on Alternating Direction Method of Multipliers

摘要: The sparse signal reconstruction of compressive sensing can be accomplished by $${l_1}$$-norm minimization, but in many existing algorithms, there are the problems low success probability and high computational complexity. To overcome these problems, an algorithm based on alternating direction method multipliers is proposed. First, using variable splitting techniques, additional introduced, which tied to original via affine constraint. Then, problem transformed into a non-constrained optimization means augmented Lagrangian multiplier method, where obtained gradient ascent according dual theory. minimization finally solved cyclic iteration with concise form, solution could projection operator, auxiliary soft threshold operator. Simulation results show that higher when compared methods, while cost required.