In an incremental optimization problem, we are given a feasible solution x(0) of an optimization problem P, and we want to make an incremental change in x(0) that will result in the greatest improvement in the objecti...
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In an incremental optimization problem, we are given a feasible solution x(0) of an optimization problem P, and we want to make an incremental change in x(0) that will result in the greatest improvement in the objective function. In this paper, we study the incremental optimization versions of six well-known network problems. We present a strongly polynomial algorithm for the incremental minimum spanning tree problem. We show that the incremental minimum cost flow problem and the incremental maximum flow problem can be solved in polynomial time using Lagrangian relaxation. We consider two versions of the incremental minimum shortest path problem, where increments are measured via arc inclusions and arc exclusions. We present a strongly polynomial time solution for the arc inclusion version and show that the arc exclusion version is NP-complete. We show that the incremental minimum cut problem is NP-complete and that the incremental minimum assignment problem reduces to the minimum exact matching problem, for which a randomized polynomial time algorithm is known.
The paper addresses a problem of finding critical paths in PERT networks (digraphs) with variable are lengths depending on a parameter. By equipping the Bellman-Ford label-correcting algorithm with variable vectorial ...
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The paper addresses a problem of finding critical paths in PERT networks (digraphs) with variable are lengths depending on a parameter. By equipping the Bellman-Ford label-correcting algorithm with variable vectorial labels depending on the parameter, we derive its version that solves the problem in O(mn(2)) time, for all possible parameter values (where nz stands for the number of arcs, and n is the number of nodes in the digraph). An application related to cyclic scheduling of tasks in a robotic cell is considered. (C) 1998 Elsevier Science B.V. All rights reserved.
We design a new label shortest path algorithm by applying the concept of a pseudo permanent label. This approach allows an algorithm to partition the set of nodes into two new sets: pseudo permanently labeled nodes an...
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We design a new label shortest path algorithm by applying the concept of a pseudo permanent label. This approach allows an algorithm to partition the set of nodes into two new sets: pseudo permanently labeled nodes and its complementary set. From this point of view, this new label method can be considered as a label setting method. Moreover, at least one node becomes permanently labeled when some nodes which belong to the set of pseudo permanently labeled nodes are scanned in each iteration of the algorithm. In the case of networks with non-negative length arcs it is easy to prove that this node has the minimum distance label among the non-pseudo permanently labeled nodes. On the other hand, it is not known during the computation which pseudo permanently labeled nodes are permanently labeled. Therefore, all distance labels are temporary and the algorithm becomes a label correcting method. Nevertheless, the proposed algorithm exhibits some nice features, such as: (1) the time bound for the running of the algorithm for a network with n nodes and m arcs is O(nm);(2) the number of node scan operations in the algorithm is less than the number of these operations in the previous label correcting algorithms as is observed in the computational experience;(3) the algorithm incorporates two new rules which allow easy detection of a negative cycle in the network;(4) the algorithm is quite simple and very easy to implement, and does not require sophisticated data structures;(5) the algorithm exhibits flexibility in the order in which the new pseudo permanently labeled nodes are scanned. The above features are possible through the application of the pseudo permanent label concept.
The rectilinear Steiner ratio is the worst-case ratio of the length of a rectilinear minimum spanning tree to the length of a rectilinear Steiner minimal tree. Hwang proved that the ratio for point sets in the plane i...
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The rectilinear Steiner ratio is the worst-case ratio of the length of a rectilinear minimum spanning tree to the length of a rectilinear Steiner minimal tree. Hwang proved that the ratio for point sets in the plane is 3/2. We provide a simple proof of the 3/2-bound.
The finger print recognition, face recognition, hand geometry, iris recognition, voice scan, signature, retina scan and several other biometric patterns are being used for recognition of an individual. Human footprint...
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The finger print recognition, face recognition, hand geometry, iris recognition, voice scan, signature, retina scan and several other biometric patterns are being used for recognition of an individual. Human footprint is one of the relatively new physiological biometrics due to its stable and unique characteristics. The texture and foot shape information of footprint offers one of the powerful means in personal recognition. This work proposes a footprint based biometric identification of an individual by extracting texture and shape based features using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) linear projection techniques. PCA is a commonly used technique for data classification and dimensionality reduction and ICA is one of the most widely used blind source separation technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. In this study PCA and ICA have been compared for footprint recognition using distance classification techniques such as Euclidean distance, city block, cosine and correlation. Experimental results show that ICA performs better than PCA for footprint recognition.
The n most vital links (or nodes) in a weighted network are those n links (nodes) whose removal from the network results in the greatest increase in shortest distance between two specified nodes. Preliminary results a...
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The finger print recognition, face recognition, hand geometry, iris recognition, voice scan, signature, retina scan and several other biometric patterns are being used for recognition of an individual. Human footprint...
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The finger print recognition, face recognition, hand geometry, iris recognition, voice scan, signature, retina scan and several other biometric patterns are being used for recognition of an individual. Human footprint is one of the relatively new physiological biometrics due to its stable and unique characteristics. The texture and foot shape information of footprint offers one of the powerful means in personal recognition. This work proposes a footprint based biometric identification of an individual by extracting texture and shape based features using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) linear projection techniques. PCA is a commonly used technique for data classification and dimensionality reduction and ICA is one of the most widely used blind source separation technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. In this study PCA and ICA have been compared for footprint recognition using distance classification techniques such as Euclidean distance, city block, cosine and correlation. Experimental results show that ICA performs better than PCA for footprint recognition.
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