Augmented Lagrangian dual for nonconvex minimax fractional programs and proximal bundle algorithms for its resolution

Hssaine Boualam; Ahmed Roubi

doi:10.3934/jimo.2022100

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2023, Volume 19, Issue 5: 3610-3636. Doi: 10.3934/jimo.2022100

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Augmented Lagrangian dual for nonconvex minimax fractional programs and proximal bundle algorithms for its resolution

Hssaine Boualam^1, , and
Ahmed Roubi^2,

1.
Ecole Nationale d'Agriculture, Meknès, Morocco
2.
Hassan First University of Settat, Faculté des Sciences et Techniques, Laboratoire MISI, Morocco

^*Corresponding author: Hssaine Boualam
^*Corresponding author: Hssaine Boualam

Received: March 2021

Revised: February 2022

Early access: June 2022

Published: May 2023

Abstract / Introduction Full Text(HTML) Figure(8) / Table(1) Related Papers Cited by

Abstract

Based on augmented Lagrangian, we propose in this paper a new dual for inequality constrained nonconvex generalized fractional programs (GFP). We give duality results under quite weak assumptions. We associate with this dual program, parametric dual subproblems and establish duality results with the usual parametric primal ones. By taking advantage of the concavity of the parametric dual functions, we propose proximal bundle-like methods that approximately solve the parametric dual subproblems, to finally solve this dual program. For some problems, these method converge linearly.

Keywords:

Mathematics Subject Classification: Primary: 90C32, 90C47, 90C46, 90C26, 49M29; Secondary: 49M37.

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Figure 1. Av. IT and Av. QP for $ c = 0.05 $

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Figure 2. Av. IT and Av. QP for $ c = 0.1 $

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Figure 3. Av. IT and Av. QP for $ c = 0.5 $

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Figure 4. Av. IT and Av. QP for $ c = 0.9 $

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Figure 5. Av. IT and Av. QP for $ \beta_k = 0.5 $

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Figure 6. Av. IT and Av. QP for $ \beta_k = 1 $

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Figure 7. Av. IT and Av. QP for $ \beta_k = 5 $

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Figure 8. Av. IT and Av. QP for $ \beta_k = 10 $

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Table 1. Results of Alg2 and Alg3 with $ n = 20 $, $ m = 10 $, $ p = 10 $

			Alg2				Alg3
$ \beta_\bf{k} $		$ c $	0.05	0.1	0.5	0.9	0.05	0.1	0.5	0.9
0.5	Av. IT		13.8	12.6	12.2	8.4	12	12.2	11.2	8.8
	Av. QP		69.2	61.2	66.4	54.4	58.2	62.4	59.6	55.8
	Av. T(s)		10.5	8.7	8.4	6.6	0.4	0.4	0.3	0.3
1	Av. IT		10	11.4	10.6	7.4	9.2	8.6	10.4	7.8
	Av. QP		42.2	50	51	45	38.6	35.4	48.8	46
	Av. T(s)		5.7	6.8	6.6	5.5	0.2	0.2	0.3	0.2
5	Av. IT		15.8	11.6	14.4	15.8	12	10.6	14.4	13.4
	Av. QP		45.2	30	37.4	57.4	32.6	28	38.8	46
	Av. T(s)		5.6	4	4.9	6.3	0.2	0.1	0.3	0.2
10	Av. IT		21.2	21.2	22.2	24	23.8	23.8	21.2	22.2
	Av. QP		36.8	36.8	39.8	54.6	44.8	44.6	37	49.2
	Av. T(s)		4.6	4.5	4.9	6.1	0.3	0.3	0.2	0.3
Opt.Val: $ \bar{\lambda} $			$ -0.3040 $				$ -0.3040 $

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