Solving survivable two-layer network design problems by metric inequalities
نویسندگان
چکیده
منابع مشابه
Solving survivable two-layer network design problems by metric inequalities
We address the problem of designing a multi-layer network with survivability requirements. We are given a two-layer network: the lower layer represents the potential physical connections that can be activated, the upper layer is made of logical connections that can be set up using physical links. We are given origin-destination demands (commodities) to be routed at the upper layer. We are also ...
متن کاملStrong inequalities for capacitated survivable network design problems
We present several classes of facet-deening inequalities to strengthen polyhedra arising as subsystems of network design problems with survivability constraints. These problems typically involve assigning capacities to a network with multicommodity demands, such that after a vertex-or edge-deletion at least some prescribed fraction of each demand can be routed.
متن کاملHierarchical Survivable Network Design Problems
We address the problem of designing two-level networks protected against single edge failures. A set of nodes must be partitioned into terminals and hubs, hubs must be connected through a backbone network, and terminals must be assigned to hubs and connected to them through access networks, being the objective to minimize the total cost. We consider two survivable structures, two-edge connected...
متن کاملStochastic Survivable Network Design Problems
We consider survivable network design problems under a two-stage stochastic model with recourse and finitely many scenarios (SSNDP). We propose two new cut-based formulations for SSNDP based on orientation properties and show that they are stronger than the undirected cut-based model. We use a two-stage branch&cut algorithm for solving the decomposed model to provable optimality. In order to ac...
متن کاملTraining Three-Layer Neural Network Classifiers by Solving Inequalities
In this paper we discuss training of three-layer neural network classifiers by solving inequalities. Namely, first we represent each class by the center of the training data belonging to the class, and determine the set of hyperplanes that separate each class into a single region. Then according to whether the center is on the positive or negative side of the hyperplane, we determine the target...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Optimization and Applications
سال: 2010
ISSN: 0926-6003,1573-2894
DOI: 10.1007/s10589-010-9364-0