Analysis of the DCA Algorithm Under the l1-αl2 Model of Compressive Sensing
In the field of compressed sensing,we prefer measurements with as little correlation as possible for the basic problem of re-covering sparse vectors from a small number of measurements.However,in reality,the calculation cost of using such l1,l2 traditional methods is higher.Therefore,in this paper,under the new model l1-αl2(0<α≤1),we use minimization of ‖x‖1-α‖x‖2 to solve the compressed sensing problem.Difference algorithm based on convex function,an iterative algorithm for solving the l1-αl2 minimization problem is obtained in this paper,it is proved that the algorithm converges to a stable point which satisfies the optimality condition.
compressed Sensingl1-αl2 minimizationDCA(Difference of Convex functions Algorithm)
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