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Displaying 141 –
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493
In this note we consider a linear-fractional programming problem with equality linear constraints. Following Rohn, we define a generalized relative sensitivity coefficient measuring the sensitivity of the optimal value for a linear program and a linear-fractional minimization problem with respect to the perturbations in the problem data.
By using an extension of Rohn's result for the linear programming case, we obtain, via Charnes-Cooper variable change, the relative sensitivity coefficient for...
Studying a critical value function in parametric nonlinear programming, we recall conditions guaranteeing that is a function and derive second order Taylor expansion formulas including second-order terms in the form of certain generalized derivatives of . Several specializations and applications are discussed. These results are understood as supplements to the well–developed theory of first- and second-order directional differentiability of the optimal value function in parametric optimization....
We consider the non-convex quadratic maximization problem subject
to the l1 unit ball constraint. The nature of the l1 norm
structure makes this problem extremely hard to analyze, and as a
consequence, the same difficulties are encountered when trying to
build suitable approximations for this problem by some tractable
convex counterpart formulations. We explore some properties of
this problem, derive SDP-like relaxations and raise open
questions.
In this paper a genetic algorithm (GA) is applied on Maximum
Betweennes Problem (MBP). The maximum of the objective function is
obtained by finding a permutation which satisfies a maximal number of
betweenness constraints. Every permutation considered is genetically coded
with an integer representation. Standard operators are used in the GA.
Instances in the experimental results are randomly generated. For smaller
dimensions, optimal solutions of MBP are obtained by total enumeration.
For those...
When a system of one-sided max-plus linear equations is inconsistent, the approximate solutions within an admissible error bound may be desired instead, particularly with some sparsity property. It is demonstrated in this paper that obtaining the sparsest approximate solution within a given error bound may be transformed in polynomial time into the set covering problem, which is known to be NP-hard. Besides, the problem of obtaining the sparsest approximate solution within a given error bound...
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