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Contents

1 [CT168]

[CT168]

Session Time & Room

CT168 (1/1) : 3C @A206 [Chair: Roi Naveiro]

Classification

CT168 (1/1) : Mathematical programming (90C)

[00912] Simulation-based Bayesian optimization over categorical covariates

Session Time & Room : 3C (Aug.23, 13:20-15:00) @A206
Type : Contributed Talk
Abstract : Optimizing black-box functions of categorical variables has important applications, including the design of biological sequences with specific properties. Bayesian optimization is widely used in this type of problem. It involves adjusting a probabilistic machine learning model of the objective and using an acquisition function to guide the optimization process. We propose a new algorithm to sequentially optimize the acquisition function inspired in simulated annealing. We address convergence issues and demonstrate its effectiveness on RNA-sequence optimization.
Classification : 90C27, 62F15, 60J20
Format : Talk at Waseda University
Author(s) :
- Roi Naveiro (CUNEF University)

[02102] Network Construction Problems

Session Time & Room : 3C (Aug.23, 13:20-15:00) @A206
Type : Contributed Talk
Abstract : Network construction problems seek to find efficient schedules of constructing edges of a new transportation network under limited resources, with the goal of minimizing a scheduling objective which is a non-decreasing function of the times when some important pairs of nodes become connected. The talk will discuss some recent results in this area of combinatorial optimization, focusing on classification and computational complexity.
Classification : 90C27
Format : Talk at Waseda University
Author(s) :
- Igor Averbakh (University of Toronto)

[01621] A Case Study on Multi-objective Fixed-charge Transportation Problem

Session Time & Room : 3C (Aug.23, 13:20-15:00) @A206
Type : Contributed Talk
Abstract : This paper investigates a case study in the field of transportation and proposes an approach for the Pareto-optimal solution of the multi-objective fixed-charge transportation problem with real life parameters represented as uncertain numbers. The model includes the knowledge and agreement as well as difference in judgements of all the experts involved. Three approaches, viz, goal programming, weighted-sum method and the fuzzy programming technique are extended using AHP, and the obtained results are analyzed and discussed.
Classification : 90C29, 90C70, 90B06, 90-10
Author(s) :
- Deepika Rani (Dr B R Ambedkar National Institute of Technology Jalandhar (INDIA))
- Shivani Saini (National Institute of Technology Jalandhar (INDIA))

[01223] Descent hybrid four-term conjugate gradient methods for unconstrained optimization

Session Time & Room : 3C (Aug.23, 13:20-15:00) @A206
Type : Industrial Contributed Talk
Abstract : Conjugate gradient method (CGM) is widely acclaimed to be efficient for solving large-scale unconstrained optimization problems. This study proposes new modified schemes that contain four terms based on the linear combination of the update parameters of classical or early methods depending on the popular two- and three-term CGMs. Hybridized methods have been found to exhibit better performance than the classical methods (Stanimirovic et al., 2018). Several other methods in this category can be found in (Adeleke et al., 2018; Osinuga and Olofin, 2018; Stanimirovic et al., 2018). In continuation of the previous results, we propose hybrid methods for the solution of large-scale unconstrained optimization problems as motivated and inspired by (Alhawarat et al., 2021, Yao et al.,2020, Stanimirovic et al.,2018). Modified methods are defined using appropriate combinations of the search directions and included parameters. In this case, our methods are hybridizations of HS and DY methods. In addition, we propose a class of Dai-Liao CGMs developed using new search directions developed using different values of included parameter. Under some certain assumptions, descent and convergence properties were established with the underlying strong Wolfe line search. The results of the new schemes showed superior performance over the existing ones in the sense of performance profiles of Dolan and More (2002). Keywords: unconstrained optimization, strong Wolfe line search, descent property, global convergence. AMS subject classification. 49J52, 49J53, 90C30 References [1] Adeleke, O. J., Osinuga, I. A. and Raji, R. A. 2021 A globally convergent hybrid FR-PRP conjugate gradient method for unconstrained optimization problems, WSEAS Transactions on Mathematics, 20, 736 -744, DOI: 10.37394/23206.2021.20.78. [2] Alhawarat, A., Alhamzi, G., Masmali, I. and Salleh, Z. 2021 A descent four-term conjugate gradient method with global convergence properties for unconstrained optimization problems, Mathematical Problems in Engineering, Volume 2021, Art. ID. 6219062, 14 pp. [3] Dai, Y. H. and Liao, L. Z. 2001 New conjugacy conditions and related nonlinear conjugate gradient methods, Applied Mathematics and Optimization, 43 (1), 87 – 101. [4] Dolan, E. and More, J. J. 2002 Benchmarking optimization software with performance profile, Mathematical Programming, 91, 201 – 213. [5] Osinuga, I. A. and Olofin, I. O. 2018 Extended hybrid conjugate gradient method for unconstrained optimization. Journal of Computer Science and its Applications, 25 (2); 166–175 [6] Stanimirovic, P. S., Ivanov, B, Djorjevic, S. and Brajevic, I. 2018 New hybrid conjugate gradient and Broyden-Fletcher-Goldfarb-Shanno conjugate gradient methods, Journal of Optimization Theory and Applications, DOI: 10.1007/s10957-018-1324-3 [7] Yao, S., Ning, L., Tu, H. and Xu, J. 2020 A one-parameter class of three-term conjugate gradient methods with an adaptive parameter choice, Optimization Methods and Software, 35 (6), 1051-1064.
Classification : 90C30, 65K05, 90C26, Nonlinear Optimization
Format : Online Talk on Zoom
Author(s) :
- Idowu Ademola Osinuga (Federal University of Agriculture, Abeokuta, Nigeria )
- Moses Oluwasegun Olubiyi (Federal University of Agriculture, Abeokuta)
- Semiu Akinpelu Ayinde (Babcock University, Ilishan-Remo)