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Improved interval DEA models with common weight

Jiasen Sun, Yajun Miao, Jie Wu, Lianbiao Cui, Runyang Zhong (2014)

Kybernetika

The traditional data envelopment analysis (DEA) model can evaluate the relative efficiencies of a set of decision making units (DMUs) with exact values. But it cannot handle imprecise data. Imprecise data, for example, can be expressed in the form of the interval data or mixtures of interval data and exact data. In order to solve this problem, this study proposes three new interval DEA models from different points of view. Two examples are presented to illustrate and validate these models.

Improved local convergence analysis of inexact Newton-like methods under the majorant condition

Ioannis K. Argyros, Santhosh George (2015)

Applicationes Mathematicae

We present a local convergence analysis of inexact Newton-like methods for solving nonlinear equations. Using more precise majorant conditions than in earlier studies, we provide: a larger radius of convergence; tighter error estimates on the distances involved; and a clearer relationship between the majorant function and the associated least squares problem. Moreover, these advantages are obtained under the same computational cost.

Impulse noise removal based on new hybrid conjugate gradient approach

Morteza Kimiaei, Majid Rostami (2016)

Kybernetika

Image denoising is a fundamental problem in image processing operations. In this paper, we present a two-phase scheme for the impulse noise removal. In the first phase, noise candidates are identified by the adaptive median filter (AMF) for salt-and-pepper noise. In the second phase, a new hybrid conjugate gradient method is used to minimize an edge-preserving regularization functional. The second phase of our algorithm inherits advantages of both Dai-Yuan (DY) and Hager-Zhang (HZ) conjugate gradient...

Incorporating the strength of MIP modeling in schedule construction

Cor A.J. Hurkens (2009)

RAIRO - Operations Research

Linear programming techniques can be used in constructing schedules but their application is not trivial. This in particular holds true if a trade-off has to be made between computation time and solution quality. However, it turns out that – when handled with care – mixed integer linear programs may provide effective tools. This is demonstrated in the successful approach to the benchmark constructed for the 2007 ROADEF computation challenge on scheduling problems furnished by France Telecom.

Inductive computations on graphs defined by clique-width expressions

Frédérique Carrère (2009)

RAIRO - Theoretical Informatics and Applications

Labelling problems for graphs consist in building distributed data structures, making it possible to check a given graph property or to compute a given function, the arguments of which are vertices. For an inductively computable function D, if G is a graph with n vertices and of clique-width at most k, where k is fixed, we can associate with each vertex x of G a piece of information (bit sequence) lab(x) of length O(log2(n)) such that we can compute D in constant time, using only the labels...

Inequality-sum : a global constraint capturing the objective function

Jean-Charles Régin, Michel Rueher (2005)

RAIRO - Operations Research - Recherche Opérationnelle

This paper introduces a new method to prune the domains of the variables in constrained optimization problems where the objective function is defined by a sum y = Σ x i , and where the integer variables x i are subject to difference constraints of the form x j - x i ≤ c . An important application area where such problems occur is deterministic scheduling with the mean flow time as optimality criteria. This new constraint is also more general than a sum constraint defined on a set of ordered variables. Classical approaches...

Inequality-sum: a global constraint capturing the objective function

Jean-Charles Régin, Michel Rueher (2010)

RAIRO - Operations Research

This paper introduces a new method to prune the domains of the variables in constrained optimization problems where the objective function is defined by a sum y = ∑xi, and where the integer variables xi are subject to difference constraints of the form xj - xi ≤ c. An important application area where such problems occur is deterministic scheduling with the mean flow time as optimality criteria. This new constraint is also more general than a sum constraint defined on a set of ordered variables....

Inexact Newton-type method for solving large-scale absolute value equation A x - | x | = b

Jingyong Tang (2024)

Applications of Mathematics

Newton-type methods have been successfully applied to solve the absolute value equation A x - | x | = b (denoted by AVE). This class of methods usually solves a system of linear equations exactly in each iteration. However, for large-scale AVEs, solving the corresponding system exactly may be expensive. In this paper, we propose an inexact Newton-type method for solving the AVE. In each iteration, the proposed method solves the corresponding system only approximately. Moreover, it adopts a new line search technique,...

Influence of modeling structure in probabilistic sequential decision problems

Florent Teichteil-Königsbuch, Patrick Fabiani (2006)

RAIRO - Operations Research

Markov Decision Processes (MDPs) are a classical framework for stochastic sequential decision problems, based on an enumerated state space representation. More compact and structured representations have been proposed: factorization techniques use state variables representations, while decomposition techniques are based on a partition of the state space into sub-regions and take advantage of the resulting structure of the state transition graph. We use a family of probabilistic exploration-like...

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