In this paper, an online algorithm, viz, online independent reduced least squares support vector regression (OIRLSSVR), is proposed based on the linear independence and the reduced technique. As opposed to some offlin...
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In this paper, an online algorithm, viz, online independent reduced least squares support vector regression (OIRLSSVR), is proposed based on the linear independence and the reduced technique. As opposed to some offline algorithms, OIRLSSVR takes the realtime advantage, which is confirmed using benchmark data sets. In comparison with online algorithm, the realtime of OIRLSSVR is also favorable. As for this point, it is tested with experiments on the benchmark data sets and a more realistic scenario namely a diesel engine example. All in all. OIRLSSVR can enhance the modeling realtime, especially for the case where the samples enter in a flow mode. (C) 2012 Elsevier Inc. All rights reserved.
In this paper, we address the following problems: Given a sequence A of n real numbers, and four parameters I, J, X and Y with I <= J and X <= Y, find the longest (or shortest) subsequence of A such that its len...
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In this paper, we address the following problems: Given a sequence A of n real numbers, and four parameters I, J, X and Y with I <= J and X <= Y, find the longest (or shortest) subsequence of A such that its length is between I and J and its sum is between X and Y. We present an online and an offline algorithm for the problems, both run in O(n log n) time, which are optimal.
This paper considers online scheduling on two unit flowshop machines, which there exists unbounded parallel-batch scheduling with incompatible job families and lookahead intervals. The unit flowshop machine means that...
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This paper considers online scheduling on two unit flowshop machines, which there exists unbounded parallel-batch scheduling with incompatible job families and lookahead intervals. The unit flowshop machine means that the processing time of any job on each machine is unit processing time. The objective is to minimize the maximum completion time. The lookahead model means that an online algorithm can foresee the information of all jobs arriving in time interval (t, t + beta] at time t. There exist incompatible job families, that is, jobs belonging to different families cannot be processed in the same batch. In this paper, we address the lower bound of the proposed problem, and provide a best possible online algorithm of competitive ratio 1+ alpha f for 0 <= beta < 1, where alpha f is the positive root of the equation (f + 1)alpha(2) + (beta + 2)alpha + beta - f = 0 and f is the number of incompatible job families which is known in advance.
In this paper we consider the online problem of packing a list of squares into one strip of width 1 and infinite length without overlap so as to minimize the required height of the packing. We derive an upper bound 5 ...
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In this paper we consider the online problem of packing a list of squares into one strip of width 1 and infinite length without overlap so as to minimize the required height of the packing. We derive an upper bound 5 on the competitive ratio for this problem.
The bin packing problem is a problem of finding an assignment of a sequence of items to a minimum number of bins, each of capacity one. An online algorithm for the bin packing problem is an algorithm that irrevocably ...
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The bin packing problem is a problem of finding an assignment of a sequence of items to a minimum number of bins, each of capacity one. An online algorithm for the bin packing problem is an algorithm that irrevocably assigns each item one by one from the head of the sequence. Gutin, Jensen, and Yeo (2006) considered a version in which all items are only of two different sizes and the online algorithm knows the two possible sizes in advance, and gave an optimal online algorithm for the case when the larger size exceeds 1/2. In this paper we provide an optimal online algorithm for some of the cases when the larger size is at most 1/2, on the basis of a framework that facilitates the design and analysis of algorithms. key words: bin packing problem, online algorithm, competitive analysis, mathematical optimization
We consider an online scheduling problem where jobs arrive one by one and each job must be irrevocably scheduled on the machines. No machine is available initially. When a job arrives, we either purchase a new machine...
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We consider an online scheduling problem where jobs arrive one by one and each job must be irrevocably scheduled on the machines. No machine is available initially. When a job arrives, we either purchase a new machine to process it or schedule it for processing on an existing machine. The objective is to minimize the sum of the makespan and the total cost of all the purchased machines. We assume that the total machine cost function is concave in the number of purchased machines. Considering both non-preemptive and preemptive variants of the problem, we prove that the competitive ratio of any non-preemptive or preemptive algorithm is at least 1.5. For the non-preemptive variant, we present an online algorithm and show that its competitive ratio is 1.6403. For the preemptive variant, we propose an online algorithm and show that its competitive ratio is 1.5654. We further prove that both competitive ratios are tight. (C) 2013 Elsevier Inc. All rights reserved.
This paper studies an online over-list model of the integrated allocation of berths and quay cranes (QCs) in container terminals with 1-lookahead ability. The objective is to minimize the maximum completion time of co...
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This paper studies an online over-list model of the integrated allocation of berths and quay cranes (QCs) in container terminals with 1-lookahead ability. The objective is to minimize the maximum completion time of container vessels, i.e., the makespan. We focus on two different types of vessels, three berths and a small number of QCs in the hybrid berth layout, with 1-lookahead ability. We propose a -competitive algorithm for the case with four cranes, a 5/4-competitive algorithm for the case with five cranes and a 4/3-competitive algorithm for the case with six cranes, respectively. All of the algorithms are proved to be optimal.
We consider the online Steiner Traveling Salesman Problem. In this problem, we are given an edge-weighted graph G = (V, E) and a subset D subset of V of destination vertices, with the optimization goal to find a minim...
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We consider the online Steiner Traveling Salesman Problem. In this problem, we are given an edge-weighted graph G = (V, E) and a subset D subset of V of destination vertices, with the optimization goal to find a minimum weight closed tour that traverses every destination vertex of D at least once. During the traversal, the salesman could encounter at most k non-recoverable blocked edges. The edge blockages are real-time, meaning that the salesman knows about a blocked edge whenever it occurs. We first show a lower bound on the competitive ratio and present an online optimal algorithm for the problem. While this optimal algorithm has non-polynomial running time, we present another online polynomial-time near optimal algorithm for the problem. Experimental results show that our online polynomial-time algorithm produces solutions very close to the offline optimal solutions. (C) 2014 Elsevier B.V. All rights reserved.
We consider two parallel machines scheduling problems with a single server. For the general case we present an online LPT algorithm with competitive ratio 2, and give a lower bound . We also apply the online LPT algor...
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We consider two parallel machines scheduling problems with a single server. For the general case we present an online LPT algorithm with competitive ratio 2, and give a lower bound . We also apply the online LPT algorithm to the special case where all the setup times are equal to 1. We show that the competitive ratio is 1.5, and no online algorithm can has a competitive ratio less than root 2.
We consider a problem as follows: Given unit weights arriving in an online manner with the total cardinality unknown, upon each arrival we decide where to place it on the unit circle in R-2. The objective is to set th...
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We consider a problem as follows: Given unit weights arriving in an online manner with the total cardinality unknown, upon each arrival we decide where to place it on the unit circle in R-2. The objective is to set the center of mass of the placed weights as close to the origin as possible. We apply competitive analysis defining the competitive difference as a performance measure. We first present an optimal strategy for placing unit weights which achieves a competitive difference of 1/5. We next consider a variant in which the destination of each weight must be chosen from a set of positions that equally divide the unit circle. We give a simple strategy whose competitive difference is 0.35. Moreover, in the offline setting, several conditions for the center of mass to lie at the origin are derived.
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