In this paper, a methodology is developed to solve a multiobjective fractional programming problem in which the coefficients of the objective functions and constraints are intervals. This model is transformed into an ...
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In this paper, a methodology is developed to solve a multiobjective fractional programming problem in which the coefficients of the objective functions and constraints are intervals. This model is transformed into an interval-free equivalent optimization problem. A new partial ordering is introduced and the relation between the original problem and the transformed problem is established using this partial ordering. The proposed methodology is illustrated through a numerical example.
A fuzzy chance-constrained linear fractional programming method was developed for agricultural water resources management under multiple uncertainties. This approach improved upon the previous programming methods, and...
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A fuzzy chance-constrained linear fractional programming method was developed for agricultural water resources management under multiple uncertainties. This approach improved upon the previous programming methods, and could reflect the ratio objective function and multiple uncertainties expressed as probability distributions, fuzzy sets, and their combinations. The proposed approach is applied to an agricultural water resources management system where many crops are considered under different precipitation years. Through the scenarios analyses, the multiple alternatives are presented. The solutions show that it is applicable to practical problems to address the crop water allocation under the precipitation variation and sustainable development with ratio objective function of the benefit and the irrigation amount. It also provides bases for identifying desired agriculture water resources management plans with reasonable benefit and irrigation schedules under crops.
The present paper deals with a solution procedure for multi objective linear fractional programming problems. An equivalent multi objective linear programming form of the problem has been formulated in the proposed me...
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The present paper deals with a solution procedure for multi objective linear fractional programming problems. An equivalent multi objective linear programming form of the problem has been formulated in the proposed methodology. Using fuzzy set theoretic approach a procedure has been explored. The proposed solution procedure has also been used to solve numerical examples. (C) 2002 Elsevier Science B.V. All rights reserved.
A second-order dual is formulated for a nondifferentiable fractional programming problem. Using the generalized second-order (F, alpha, rho, d)-convexity assumptions on the functions involved, weak, strong and convers...
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A second-order dual is formulated for a nondifferentiable fractional programming problem. Using the generalized second-order (F, alpha, rho, d)-convexity assumptions on the functions involved, weak, strong and converse duality theorems are established in order to relate the primal and dual problems. (C) 2010 Published by Elsevier Ltd
Several algorithms to solve the generalized fractional program are summarized and compared numerically in the linear case. These algorithms are iterative procedures requiring the solution of a linear programming probl...
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Several algorithms to solve the generalized fractional program are summarized and compared numerically in the linear case. These algorithms are iterative procedures requiring the solution of a linear programming problem at each iteration in the linear case. The most efficient algorithm is obtained by marrying the Newton approach within the Dinkelbach approach for fractional programming. [ABSTRACT FROM AUTHOR]
In this paper, by using the properties of the epigraph of the conjugate functions, we introduce some closedness conditions and investigate some characterizations of these closedness conditions. Then, by using these cl...
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In this paper, by using the properties of the epigraph of the conjugate functions, we introduce some closedness conditions and investigate some characterizations of these closedness conditions. Then, by using these closedness conditions, we obtain some Farkas-type results for a constrained fractional programming problem with DC functions. We also show that our results encompass as special cases some programming problems considered in the recent literature.
In this paper, we present a new algorithm for quasi-Newton type trust region subproblem with a conic model solving unconstrained optimization problems. In the algorithm, we propose to use the method of dealing with fr...
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In this paper, we present a new algorithm for quasi-Newton type trust region subproblem with a conic model solving unconstrained optimization problems. In the algorithm, we propose to use the method of dealing with fractional programming into the conic model as the conic model is of fractional form and the method can find the real solution of subproblem not an approximate solution like the available method. This new approach can be easily generalized to any optimization method which its approximate subproblem with fractional expression, whose approximate effectiveness to the class of highly vibrating objective functions is superior to that of the normal quadratic model. This idea is what the author want to develop through the conic model as a special example in the paper. The preliminary numerical test shows that the new method is more effective. (c) 2006 Elsevier Inc. All rights reserved.
Separability is an important criterion for measuring the degree of overlap between different features in pattern recognition. Stronger separability indicates clearer feature margins, facilitating classification of sam...
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Separability is an important criterion for measuring the degree of overlap between different features in pattern recognition. Stronger separability indicates clearer feature margins, facilitating classification of samples into their respective classes. Therefore, if the separability of radar target echoes within the feature space increases, the likelihood of correct classification increases. A waveform with enhanced separability offers more reliable discrimination for specific targets and highlights their unique aspects. Accordingly, this paper proposes radar waveform design methods driven by target echo pattern separability, i.e., the convolution of the target high-resolution range profiles (HRRP)/target impulse response (TIR) and transmit waveform. First, from the views of local HRRP/TIR manifold structure preservation and inter-class distance enlargement, we construct a minmax based fractional optimization model with non-convex and non-linear orthonormality and constant modulus (CM) constraints. Then, the derived waveform design solution is computed iteratively via subproblem division, fraction simplification, and high-order polynomial optimization. Furthermore, we extend our study to a linear discriminant analysis-based waveform design model, which incorporates minmax intra-class distance and inter-class distance metrics to maximize separability of all classes from a worst-case perspective. We utilize simulated datasets based on the scattering point model as well as electromagnetic simulation and MSTAR CSV datasets to evaluate the performance of our waveform design approaches.
In this paper we study a parametric approach, one of the resolution methods for solving integer linear fractional programming (ILFP) problems in which all functions in the objective and constraints are linear and all ...
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In this paper we study a parametric approach, one of the resolution methods for solving integer linear fractional programming (ILFP) problems in which all functions in the objective and constraints are linear and all variables are integers. We develop a novel complexity bound of Newton's method applied to ILFP problems when variables are bounded. The analytical result for the worst-case performance shows that the number of iterations of Newton's algorithm to find an optimal solution of the ILFP problem is polynomially bounded. We also propose a Hybrid-Newton algorithm and empirically show that it is relatively faster and more robust than the Newton algorithm under various data scenarios. To illustrate the applicability of our algorithm and provide concrete managerial prescriptions, we consider a case study of a road maintenance planning problem in Seoul, Korea. The results show that our fractional efficiency measure is capable of obtaining the maximum cost-efficient lifetime of a road given a limited maintenance budget. (C) 2019 Elsevier B.V. All rights reserved.
In this note, we consider a discrete fractional programming in light of a decision problem where limited number of indivisible resources are allocated to several heterogeneous projects to maximize the ratio of total p...
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In this note, we consider a discrete fractional programming in light of a decision problem where limited number of indivisible resources are allocated to several heterogeneous projects to maximize the ratio of total profit to total cost. For each project, both profit and cost are solely determined by the amount of resources allocated to it. Although the problem can be reformulated as a linear program with variables and constraints, we further show that it can be efficiently solved by induction in time. In application, this method leads to an algorithm for assortment optimization problem under nested logit model with cardinality constraints (Feldman and Topaloglu, Oper Res 63: 812-822, 2015).
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