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It is well known that a large neighborhood interior point algorithm for linear optimization performs much better in implementation than its small neighborhood counterparts. One of the key elements of interior point algorithms is how to update the barrier parameter. The main goal of this paper is to introduce an “adaptive” long step interior-point algorithm in a large neighborhood of central path using the classical logarithmic barrier function having iteration complexity analogous to the classical...
The multiparametric 0-1-Integer Programming (0-1-IP)
problem relative to the objective function is a family of 0-1-IP problems
which are related
by having
identical constraint matrix and right-hand-side
vector. In this paper we present an algorithm to perform a complete
multiparametric analysis relative to a generalized min max objective
function such that the min sum and min max are particular cases.
The multiparametric min max 0-1-Integer Programming (0-1-IP) problem relative to the objective function is a family of min max 0-1-IP problems which are related by having identical constraint matrix and right-hand-side vector. In this paper we present an algorithm to perform a complete multiparametric analysis relative to the objective function.
The multiparametric min max 0-1-Integer Programming (0-1-IP) problem relative to the objective function is a family of min max 0-1-IP problems which are related by having identical constraint matrix and right-hand-side vector. In this paper we present an algorithm to perform a complete
multiparametric analysis relative to the objective function.
In the present paper a complete procedure for solving Multiple Objective Integer Linear Programming Problems is presented. The algorithm can be regarded as a corrected form and an alternative to the method that was proposed by Gupta and Malhotra. A numerical illustration is given to show that this latter can miss some efficient solutions. Whereas, the algorithm stated bellow determines all efficient solutions without missing any one.
In the present paper a complete procedure for solving Multiple
Objective Integer Linear Programming Problems is presented. The algorithm
can be regarded as a corrected form and an alternative to the method that
was proposed by Gupta and Malhotra. A numerical illustration is given to
show that this latter can miss some efficient solutions. Whereas, the
algorithm stated bellow determines all efficient solutions without
missing any one.
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