We consider the problem of rate-distortion optimized streaming of packetized multimedia data over a single quality-of-service network using feedback and retransmissions. For a single data unit, we prove that the probl...
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We consider the problem of rate-distortion optimized streaming of packetized multimedia data over a single quality-of-service network using feedback and retransmissions. For a single data unit, we prove that the problem is NP-hard and provide efficient branch and bound algorithms that are much faster than the previously best solution based on dynamic programming. For a group of interdependent data units, we show how to compute optimal solutions with branch and bound algorithms. The branch and bound algorithms for a group of data units are much slower than the current state of the art, a heuristic technique known as sensitivity adaptation. However, in many real-world situations, they provide a significantly better rate-distortion performance.
The maximum clique problem (MCP) is a fundamental problem in combinatorial optimization which finds important applications in real-word. This paper describes two new efficient branch-and-bound maximum clique algorithm...
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The maximum clique problem (MCP) is a fundamental problem in combinatorial optimization which finds important applications in real-word. This paper describes two new efficient branch-and-bound maximum clique algorithms NK-MaxClique and MMCQ, designed for solving MCP. We define some pruning conditions based on core numbers and vertex ordering to efficiently remove many of the search space. With respect to this ordering, the algorithms consider the vertices respectively to find the corresponding maximum clique in subproblems. Simulation results demonstrate that the algorithms outperform the previous well-known algorithms for many instances when applied to DIMACS benchmark and random graphs.
An approximate stochastic model of branch-and-boundalgorithms with a best-first search is proposed. The average memory space required is estimated and the average number of subproblems expanded before the process ter...
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An approximate stochastic model of branch-and-boundalgorithms with a best-first search is proposed. The average memory space required is estimated and the average number of subproblems expanded before the process terminates is predicted. Both of these measures are exponentials of sublinear exponent. In addition, a comparison is made between the number of subproblems expanded in a best-first search and that expanded in a depth-first search. Depth-first search is found to have computational complexity comparable to best-first search when the lower bound function is highly accurate or highly inaccurate; otherwise, the best-fit search is usually preferable. The results derived are useful in examining the efficient evaluation of branch-and-boundalgorithms in a virtual memory environment. They also verify that approximations are very effective in lowering the total number of iterations.
A new search strategy, called depth-msearch, is proposed for branch-and-boundalgorithms, wheremis a parameter to be set by the user. In particular, depth-1 search is equivalent to the conventional depth-first search,...
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A new search strategy, called depth-msearch, is proposed for branch-and-boundalgorithms, wheremis a parameter to be set by the user. In particular, depth-1 search is equivalent to the conventional depth-first search, and depth-∞ search is equivalent to the general heuristic search (including best-bound search as a special case). It is confirmed by computational experiment that the performance of depth-msearch continuously changes from that, of depth-first search to that of heuristic search, whenmis changed from 1 to ∞. The exact upper bound on the size of the required memory space is derived and shown to be bounded byO(nm), wherenis the problem size. Some methods for controllingmduring computation are also proposed and compared, to carry out the entire computation within a given memory space bo
This paper develops a branch and bound algorithm for the two-stage assembly scheduling problem. In this problem, there are m machines at the first stage, each of which produces a component of a job. When all pn compon...
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This paper develops a branch and bound algorithm for the two-stage assembly scheduling problem. In this problem, there are m machines at the first stage, each of which produces a component of a job. When all pn components are available, a single assembly machine at the second stage completes the job. The objective is to schedule the jobs on the machines so that the maximum completion time, or makespan, is minimized. A lower bound based on solving an artificial two-machine flow shop problem is derived. Also, several dominance theorems are established and incorporated into the branch and bound algorithm. Computational experience with the algorithm is reported for problems with up to 8000 jobs and 10 first-stage machines. (C) 1997 Published by Elsevier Science B.V.
In the biobjective branch-and-bound literature, a key ingredient is objective branching, that is, to create smaller and disjoint subproblems in the objective space, obtained from the partial dominance of the lower bou...
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In the biobjective branch-and-bound literature, a key ingredient is objective branching, that is, to create smaller and disjoint subproblems in the objective space, obtained from the partial dominance of the lower bound set by the upper bound set. When considering three or more objective functions, however, applying objective branching becomes more complex, and its benefit has so far been unclear. In this paper, we investigate several ingredients that allow us to better exploit objective branching in a multiobjective setting. We extend the idea of probing to multiple objectives for the 0-1 case, enhance it in several ways, and show that, when coupled with objective branching, it results in significant speedups in terms of CPU times. We also show how to adapt it to the general integer case. Furthermore, we investigate cut generation based on the objective branching constraints. Besides, we generalize the best bound idea for node selection to multiple objectives, and we show that the proposed rules outperform, in the multiobjective literature, the commonly employed depth-first and breadth-first strategies. We also analyze problem specific branching rules. We test the proposed ideas on available benchmark instances for three problem classes with three and four objectives, namely, the capacitated facility location problem, the uncapacitated facility location problem, and the knapsack problem. Our enhanced multiobjective branch-and-bound algorithm outperforms the best existing branch-and-bound-based approach and is the first to obtain competitive and even slightly better results than a state-of-the-art objective space search method on a subset of the problem classes.
Reversible variable length codes (RVLCs) have been extensively studied. In recent years, many new coding standards such as H.263 MPEG-4 and JPEG-2000 have adopted RVLCs to enhance their error resilient capability. Thi...
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Reversible variable length codes (RVLCs) have been extensively studied. In recent years, many new coding standards such as H.263 MPEG-4 and JPEG-2000 have adopted RVLCs to enhance their error resilient capability. This paper presents a novel algorithm that can construct efficient RVLCs. Distinct from previous Huffman-based schemes, the proposed algorithm employs the branch-and-bound strategy to generate RVLCs. This strategy not only improvesthe coding efficiency but also provides a flexible codeword selection. The experimental results show that the RVLCs obtained by our algorithm outperform all existing RVLCs.
The paper considers the problem of finding a spanning arborescence on a directed network whose arc costs are partially known. It is assumed that each arc cost can take on values from a known interval defining a possib...
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The paper considers the problem of finding a spanning arborescence on a directed network whose arc costs are partially known. It is assumed that each arc cost can take on values from a known interval defining a possible economic scenario. In this context, the problem of finding the spanning arborescence which better approaches to that of minimum overall cost under each possible scenario is studied. The minimax regret criterion is proposed in order to obtain such a robust solution of the problem. As it is shown, the bounds on the optimal value of the minimax regret optimization problem obtained in a previous paper, can be used here in a branch and bound algorithm in order to give an optimal solution. The computational behavior of the algorithm is tested through numerical experiments.
This paper presents a modified branch and bound (B&B) algorithm called, the branch, bound, and Remember (BB&R) algorithm, which uses the Distributed Best First Search (DBFS) exploration strategy for solving th...
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This paper presents a modified branch and bound (B&B) algorithm called, the branch, bound, and Remember (BB&R) algorithm, which uses the Distributed Best First Search (DBFS) exploration strategy for solving the 1|r (i) |at (i) scheduling problem, a single machine scheduling problem where the objective is to find a schedule with the minimum total tardiness. Memory-based dominance strategies are incorporated into the BB&R algorithm. In addition, a modified memory-based dynamic programming algorithm is also introduced to efficiently compute lower bounds for the 1 vertical bar r (i) vertical bar at (i) scheduling problem. Computational results are reported, which shows that the BB&R algorithm with the DBFS exploration strategy outperforms the best known algorithms reported in the literature.
This paper analyzes the applicability and relative efficiency of four pruning rules for finding k -nearest neighbors by a branch and bound algorithm. Results of experimental simulations with various clustering schemes...
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This paper analyzes the applicability and relative efficiency of four pruning rules for finding k -nearest neighbors by a branch and bound algorithm. Results of experimental simulations with various clustering schemes and data distributions are included.
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