Improved Stochastic Gradient Matching Pursuit Algorithm Based on the Soft-Thresholds Selection

Algorithm 2

Proposed algorithm.

	Input:
	Sparsity level
	Sensing matrix
	Observation vector
	Block size
	Tolerance used to exit loop
	Maximum number of iterations
	Initialize parameter:
	{initialize signal approximation}
	{loop index}
	{while loop flag}
	{empty preliminary index set}
	{empty candidate index set}
	{empty support index set}
	{number of block}
	While (∼done)
	(1) Randomize
	(2) Computation of gradient
	(3) Identify the large 2s components
	(4) Soft-threshold selection strategy
	(5) Merge to update the candidate index set
	Reliability verification conditions 1
	If
	else
	if
	end
	break;
	end
	(6) Estimation of signal by least square method
	(7) Prune to obtain current support index set
	Reliability verification conditions 2
	If ()
	else
	end
	(8) Update
	(9) Check the iteration stopping condition
	If
	done = quit iteration
	end
	end
	Output: (s-sparse approximation of signal )