Stochastic approximation method using diagonal positive-definite matrices for convex optimization with fixed point constraints

Iiduka, Hideaki

doi:10.1186/s13663-021-00695-3

Fixed Point Theory and Algorithms for Sciences and Engineering

Table 5 Multiple comparison for elapsed time for the sparsity learning methods applied to the datasets in Table 1 using Tukey–Kramer’s HSD test at the 5% significance level (“meandiffs” indicates the pairwise mean differences between Groups 1 and 2, “p-adj” indicates the adjusted p-value, and “Lower” (resp. “Upper”) indicates the lower (resp. upper) value of the confidence interval for the pairwise mean differences)

From: Stochastic approximation method using diagonal positive-definite matrices for convex optimization with fixed point constraints

Group 1	Group 2	meandiffs	p-adj	Lower	Upper	Reject
C1	C2	0.0019	0.9	−3.0351	3.0389	FALSE
C1	C3	0.0142	0.9	−3.0227	3.0512	FALSE
C1	C4	0.0043	0.9	−3.0327	3.0413	FALSE
C1	D1	0.0003	0.9	−3.0367	3.0372	FALSE
C1	D2	0.0026	0.9	−3.0344	3.0395	FALSE
C1	D3	0.3937	0.9	−2.6433	3.4307	FALSE
C1	D4	0.0258	0.9	−3.0111	3.0628	FALSE
C1	D5	0.0366	0.9	−3.0003	3.0736	FALSE
C1	D6	0.323	0.9	−2.714	3.3599	FALSE
C1	SG	9.1275	0.001	6.0905	12.1645	TRUE
C2	C3	0.0123	0.9	−3.0246	3.0493	FALSE
C2	C4	0.0024	0.9	−3.0346	3.0394	FALSE
C2	D1	−0.0016	0.9	−3.0386	3.0353	FALSE
C2	D2	0.0007	0.9	−3.0363	3.0376	FALSE
C2	D3	0.3918	0.9	−2.6452	3.4288	FALSE
C2	D4	0.0239	0.9	−3.013	3.0609	FALSE
C2	D5	0.0347	0.9	−3.0022	3.0717	FALSE
C2	D6	0.3211	0.9	−2.7159	3.358	FALSE
C2	SG	9.1256	0.001	6.0886	12.1626	TRUE
C3	C4	−0.0099	0.9	−3.0469	3.027	FALSE
C3	D1	−0.014	0.9	−3.051	3.023	FALSE
C3	D2	−0.0117	0.9	−3.0486	3.0253	FALSE
C3	D3	0.3795	0.9	−2.6575	3.4164	FALSE
C3	D4	0.0116	0.9	−3.0254	3.0485	FALSE
C3	D5	0.0224	0.9	−3.0146	3.0593	FALSE
C3	D6	0.3087	0.9	−2.7282	3.3457	FALSE
C3	SG	9.1132	0.001	6.0763	12.1502	TRUE
C4	D1	−0.004	0.9	−3.041	3.0329	FALSE
C4	D2	−0.0017	0.9	−3.0387	3.0352	FALSE
C4	D3	0.3894	0.9	−2.6476	3.4264	FALSE
C4	D4	0.0215	0.9	−3.0155	3.0585	FALSE
C4	D5	0.0323	0.9	−3.0046	3.0693	FALSE
C4	D6	0.3187	0.9	−2.7183	3.3556	FALSE
C4	SG	9.1232	0.001	6.0862	12.1602	TRUE
D1	D2	0.0023	0.9	−3.0347	3.0393	FALSE
D1	D3	0.3935	0.9	−2.6435	3.4304	FALSE
D1	D4	0.0256	0.9	−3.0114	3.0625	FALSE
D1	D5	0.0364	0.9	−3.0006	3.0733	FALSE
D1	D6	0.3227	0.9	−2.7143	3.3597	FALSE
D1	SG	9.1272	0.001	6.0903	12.1642	TRUE
D2	D3	0.3911	0.9	−2.6458	3.4281	FALSE
D2	D4	0.0232	0.9	−3.0137	3.0602	FALSE
D2	D5	0.0341	0.9	−3.0029	3.071	FALSE
D2	D6	0.3204	0.9	−2.7166	3.3574	FALSE
D2	SG	9.1249	0.001	6.088	12.1619	TRUE
D3	D4	−0.3679	0.9	−3.4049	2.6691	FALSE
D3	D5	−0.3571	0.9	−3.3941	2.6799	FALSE
D3	D6	−0.0707	0.9	−3.1077	2.9662	FALSE
D3	SG	8.7338	0.001	5.6968	11.7707	TRUE
D4	D5	0.0108	0.9	−3.0262	3.0478	FALSE
D4	D6	0.2972	0.9	−2.7398	3.3341	FALSE
D4	SG	9.1017	0.001	6.0647	12.1386	TRUE
D5	D6	0.2863	0.9	−2.7506	3.3233	FALSE
D5	SG	9.0909	0.001	6.0539	12.1278	TRUE
D6	SG	8.8045	0.001	5.7676	11.8415	TRUE

Back to article page