This paper deals with the fractional linear programming problem in which input data can vary in some given real compact intervals. The aim is to compute the exact range of the optimal value function. A method is provi...
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This paper deals with the fractional linear programming problem in which input data can vary in some given real compact intervals. The aim is to compute the exact range of the optimal value function. A method is provided for the situation in which the feasible set is described by a linear interval system. Moreover, certain dependencies between the coefficients in the nominators and denominators can be involved. Also, we extend this approach for situations in which the same vector appears in different terms in nominators and denominators. The applicability of the approaches developed is illustrated in the context of the analysis of hospital performance.
The study confirms the convexity of the joint numerical range of any k real-valued linear functions on the n x p complex Stiefel manifold under the condition k <= 2n-2p+1. Revealing the hidden convexity of fraction...
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The study confirms the convexity of the joint numerical range of any k real-valued linear functions on the n x p complex Stiefel manifold under the condition k <= 2n-2p+1. Revealing the hidden convexity of fractional linear programming on the complex Stiefel manifold, a first-time study, serves as an impactful application. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar
The problem of sorting unsigned permutations by double-cut-and-joins (SBD) arises when we perform the double-cut-and-join (DCJ) operations on pairs of unichromosomal genomes without the gene strandedness information. ...
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The problem of sorting unsigned permutations by double-cut-and-joins (SBD) arises when we perform the double-cut-and-join (DCJ) operations on pairs of unichromosomal genomes without the gene strandedness information. In this paper we show it is a NP-hard problem by reduction to an equivalent previously-known problem, called breakpoint graph decomposition (BGD), which calls for a largest collection of edge-disjoint alternating cycles in a breakpoint graph. To obtain a better approximation algorithm for the SBD problem, we made a suitable modification to Lin and Jiang's algorithm which was initially proposed to approximate the BGD problem, and then carried out a rigorous performance analysis via fractional linear programming. The approximation ratio thus achieved for the SBD problem is , for any positive epsilon.
Cell sectorization is used extensively to increase the capacity of cellular systems;however, intersector interference caused by nonideal sector antennas limits full realization of sectorization benefits, In this paper...
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Cell sectorization is used extensively to increase the capacity of cellular systems;however, intersector interference caused by nonideal sector antennas limits full realization of sectorization benefits, In this paper, we propose a new technique for sector beam synthesis using phased antenna arrays. We adopt the criterion of minimizing intersector interference, subject to a peak-to-peak ripple constraint on the in-sector power pattern, Spectral factorization permits a reparameterization yielding an easily solved fractionallinear program. The proposed procedure accommodates nonisotropic antenna elements, arbitrary element spacing, and arbitrary sector boundaries, but is limited to uniformly spaced linear arrays. To demonstrate the utility of the proposed method, it is used to predict antenna array size requirements for a direct-sequence code-division multiple-access (DS-CDMA) cellular system.
When the parameter space is multidimensional, to elicit the joint prior distribution is a very difficult task. An accessible prior information might then be the class of prior distributions with given one-dimensional ...
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When the parameter space is multidimensional, to elicit the joint prior distribution is a very difficult task. An accessible prior information might then be the class of prior distributions with given one-dimensional marginals. Unfortunately, even in bidimensional parameter spaces, the variational problems encountered in the Bayesian analysis of this class have not yet been solved. This paper is devoted to studying two approximations to these variational problem based on the observation that the above class of priors can be seen as the class of priors with specified probabilities for a given (non-countable) collection of sets. Illustrations, including a recent clinical trial (ECMO), are given.
We present a new projective interior point method for linearprogramming with unknown optimal value. This algorithm requires only that an interior feasible point be provided. It generates a strictly decreasing sequenc...
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We present a new projective interior point method for linearprogramming with unknown optimal value. This algorithm requires only that an interior feasible point be provided. It generates a strictly decreasing sequence of objective values and within polynomial time, either determines an optimal solution, or proves that the problem is unbounded. We also analyze the asymptotic convergence rate of our method and discuss its relationship to other polynomial time projective interior point methods and the affine scaling method.
We demonstrate that Karmarkar's projective algorithm is fundamentally an algorithm for fractional linear programming on the simplex. Convergence for the latter problem is established assuming only an initial lower...
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We demonstrate that Karmarkar's projective algorithm is fundamentally an algorithm for fractional linear programming on the simplex. Convergence for the latter problem is established assuming only an initial lower bound on the optimal objective value. We also show that the algorithm can be easily modified so as to assure monotonicity of the true objective values, while retaining all global convergence properties. Finally, we show how the monotonic algorithm can be used to obtain an initial lower bound when none is otherwise available.
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