A customized proximal point algorithm for stable principal component pursuit with nonnegative constraint

Journal of Inequalities and Applications

Table 2 Comparison of the iteration numbers between APGM and Algorithm 1

n	Algorithm	\(\boldsymbol{R_{r} =0.01}\) , \(\boldsymbol{C_{r} =0.01}\)	\(\boldsymbol{R_{r} =0.02}\) , \(\boldsymbol{C_{r} =0.02}\)	\(\boldsymbol{R_{r} =0.03}\) , \(\boldsymbol{C_{r} =0.03}\)
n	Algorithm	min/avg/max	min/avg/max	min/avg/max
100	APGM	30/43/70	62/68/74	67/ 72/77
100	Algorithm 1	49/60/72	56/62/73	60/ 63/66
150	APGM	43/53/59	60/63/64	53/ 55/57
150	Algorithm 1	47/57/71	44/48/51	46/ 49/51
200	APGM	45/50/54	51/53/57	43/ 45/47
200	Algorithm 1	42/47/59	42/43/44	44/ 44/45
250	APGM	47/51/53	43/45/46	36/ 37/38
250	Algorithm 1	41/44/50	41/41/42	43/ 44/44
300	APGM	46/47/48	39/40/41	34/ 34/34
300	Algorithm 1	39/40/40	41/41/42	44/ 44/44
400	APGM	41/43/43	33/33/33	32/ 33/35
400	Algorithm 1	39/40/40	42/42/42	45/ 45/46
500	APGM	36/37/38	33/33/33	33/ 33/33
500	Algorithm 1	40/40/40	43/43/43	45/ 45/46