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 4 Multiple comparison for accuracies 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	0.7823	0.9	−6.969	8.5335	FALSE
C1	C3	−0.8189	0.9	−8.5702	6.9323	FALSE
C1	C4	−3.5273	0.9	−11.2785	4.2239	FALSE
C1	D1	−15.4512	0.001	−23.2024	−7.6999	TRUE
C1	D2	−12.3071	0.001	−20.0583	−4.5559	TRUE
C1	D3	−0.7095	0.9	−8.4607	7.0417	FALSE
C1	D4	−5.4384	0.4642	−13.1897	2.3128	FALSE
C1	D5	−7.5201	0.0668	−15.2713	0.2311	FALSE
C1	D6	1.1391	0.9	−6.6122	8.8903	FALSE
C1	SG	1.3916	0.9	−6.3596	9.1428	FALSE
C2	C3	−1.6012	0.9	−9.3524	6.15	FALSE
C2	C4	−4.3096	0.7575	−12.0608	3.4416	FALSE
C2	D1	−16.2334	0.001	−23.9847	−8.4822	TRUE
C2	D2	−13.0894	0.001	−20.8406	−5.3382	TRUE
C2	D3	−1.4918	0.9	−9.243	6.2594	FALSE
C2	D4	−6.2207	0.2564	−13.9719	1.5305	FALSE
C2	D5	−8.3023	0.0241	−16.0536	−0.5511	TRUE
C2	D6	0.3568	0.9	−7.3944	8.108	FALSE
C2	SG	0.6093	0.9	−7.1419	8.3605	FALSE
C3	C4	−2.7084	0.9	−10.4596	5.0428	FALSE
C3	D1	−14.6322	0.001	−22.3834	−6.881	TRUE
C3	D2	−11.4882	0.001	−19.2394	−3.737	TRUE
C3	D3	0.1094	0.9	−7.6418	7.8606	FALSE
C3	D4	−4.6195	0.6775	−12.3707	3.1317	FALSE
C3	D5	−6.7011	0.1642	−14.4524	1.0501	FALSE
C3	D6	1.958	0.9	−5.7932	9.7092	FALSE
C3	SG	2.2105	0.9	−5.5407	9.9617	FALSE
C4	D1	−11.9238	0.001	−19.6751	−4.1726	TRUE
C4	D2	−8.7798	0.0121	−16.531	−1.0286	TRUE
C4	D3	2.8178	0.9	−4.9334	10.569	FALSE
C4	D4	−1.9111	0.9	−9.6623	5.8401	FALSE
C4	D5	−3.9928	0.8393	−11.744	3.7585	FALSE
C4	D6	4.6664	0.6654	−3.0848	12.4176	FALSE
C4	SG	4.9189	0.6002	−2.8323	12.6701	FALSE
D1	D2	3.144	0.9	−4.6072	10.8953	FALSE
D1	D3	14.7416	0.001	6.9904	22.4929	TRUE
D1	D4	10.0127	0.0016	2.2615	17.7639	TRUE
D1	D5	7.9311	0.0398	0.1799	15.6823	TRUE
D1	D6	16.5902	0.001	8.839	24.3414	TRUE
D1	SG	16.8427	0.001	9.0915	24.594	TRUE
D2	D3	11.5976	0.001	3.8464	19.3488	TRUE
D2	D4	6.8687	0.1379	−0.8825	14.6199	FALSE
D2	D5	4.787	0.6343	−2.9642	12.5383	FALSE
D2	D6	13.4462	0.001	5.6949	21.1974	TRUE
D2	SG	13.6987	0.001	5.9475	21.4499	TRUE
D3	D4	−4.7289	0.6493	−12.4801	3.0223	FALSE
D3	D5	−6.8106	0.1467	−14.5618	0.9407	FALSE
D3	D6	1.8486	0.9	−5.9027	9.5998	FALSE
D3	SG	2.1011	0.9	−5.6501	9.8523	FALSE
D4	D5	−2.0816	0.9	−9.8329	5.6696	FALSE
D4	D6	6.5775	0.1849	−1.1737	14.3287	FALSE
D4	SG	6.83	0.1437	−0.9212	14.5812	FALSE
D5	D6	8.6591	0.0145	0.9079	16.4104	TRUE
D5	SG	8.9117	0.0099	1.1604	16.6629	TRUE
D6	SG	0.2525	0.9	−7.4987	8.0037	FALSE

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