G. Van Meerbergen, Maarten Vergauwen, Marc Pollefeys et al. · 2002
In this paper, a new hierarchical stereo algorithm is presented. The algorithm matches individual pixels in corresponding scanlines by minimizing a cost function. Several cost functions are compared. The algorithm achieves a tremendous gain in speed and memory requirements by implementing it hierarchically. The images are down sampled an optimal number of times and the disparity map of a lower level is used as 'offset' disparity map at a higher level. An important contribution consists of the complexity analysis of the algorithm. It is shown that this complexity is independent of the disparity range. This result is also used to determine the-optimal number of down sample levels. This speed gain results in the ability to use more complex (compute intensive) cost functions that deliver high quality disparity maps. Another advantage of this algorithm is that cost functions can be chosen independent of the optimisation algorithm. Finally, the algorithm was carefully implemented so that a minimal amount of memory is used. It has proven its efficiency on large images with a high disparity range as well as its quality. Examples are given in this paper.
Cited by 13
科林 杨 · Software Engineering and Applications · 2024
Cited by 0
Chaoqi Lv, Weiming Wang, Ming Gao · 2009
The paper had put the idea about the principle of optimizing dynamic programming to the log pattern matching algorithm, made the best dynamic matching algorithm, that is, by matching local sub-problems to solve, in view of the number of different rules, the matching algorithm will find the best combination to achieve the overall strategy of the optimal solution.
Cited by 1
Sasinowski, Jay K. Strosnider · 2007
This correspondence presents an algorithm for optimally partitioning two-level memory systems for deterministic, real-time performance. Further, the algorithm finds the optimal solution in polynomial time, which supports on-line reconfiguration of memory resources in response to changing requirements. Index Terms - Real-Time, Multi-Level Memories, Schedulability, Utilization, Dynamic programming. I INTRODUCTION Many computing systems have multi-level memory hierarchies with small, fast memory close to the processing engine backed by a larger, slower memory. The purpose for such hierarchies is to balance the trade-offs between the cost of faster memories and the performance gains which they provide. The faster memories usually take the form of caches or local RAM, while the slower memories are typically system memory, ROM, or disk. For example, systems such as the Intel i960CA or digital signal processors like the AT&T DSP3210 or the Texas Instruments TMS320XX use high speed on-chip R...
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Van Meerbergen Vergauwen, G. Van Meerbergen, M. Vergauwen et al. · 2002
In this paper, a new hierarchical stereo algorithm is presented. The algorithm matches individual pixels in corresponding scanlines by minimizing a cost function. Several cost functions are compared. The algorithm achieves a tremendous gain in speed and memory requirements by implementing it hierarchically. The images are downsampled an optimal number of times and the disparity map of a lower level is used as 'offset' disparity map at a higher level. An important contribution consists of the complexity analysis of the algorithm. It is shown that this complexity is independent of the disparityrange. This result is also used to determine the optimal number of downsample levels. This speed gain results in the ability to use more complex (compute intensive) cost functions that deliver high quality disparity maps. Another advantage of this algorithm is that cost functions can be chosen independent of the optimisation algorithm. The algorithm in this paper is symmetric, i.e. exactly the same matches are found if left and right image are swapped. Finally, the algorithm was carefully implemented so that a minimal amount of memory is used. It has proven its efficiency on large images with a high disparity range as well as its quality. Examples are given in this paper.
Cited by 0
Shaorui Zhou, Ming Cai, Xiaopo Zhuo · Artificial intelligence · 2022
We propose an efficient machine learning algorithm for two-stage stochastic programs. This machine learning algorithm is termed as projected stochastic hybrid learning algorithm, and consists of stochastic sub-gradient and piecewise linear approximation methods. We use the stochastic sub-gradient and sample information to update the piecewise linear approximation on the objective function. Then we introduce a projection step, which implemented the sub-gradient methods, to jump out from a local optimum, so that we can achieve a global optimum. By the innovative projection step, we show the convergent property of the algorithm for general two-stage stochastic programs. Furthermore, for the network recourse problem, our algorithm can drop the projection steps, but still maintains the convergence property. Thus, if we properly construct the initial piecewise linear functions, the pure piecewise linear approximation method is convergent for general two-stage stochastic programs. The proposed approximate dynamic programming algorithm overcomes the high dimensional state variables using methods from machine learning, and its logic capture the critical ability of the network structure to anticipate the impact of decisions now on the future. The optimization framework, which is carefully calibrated against historical performance, make it possible to introduce changes in the decisions and capture the collective intelligence of the experienced decisions. Computational results indicate that the algorithm exhibits rapid convergence.
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星科 李 · Artificial Intelligence and Robotics Research · 2019
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Michael S. Waterman, Thomas H. Byers · 1985
Just after he introduced dynamic programming, Richard Bellman with R. Kalaba in 1960 gave a method for finding Kth best policies. Their method has been modified since then, but it is still not practical for many problems. This paper describes a new technique which modifies the usual backtracking procedure and lists all near-optimal policies. This practical algorithm is very much in the spirit of the original formulation of dynamic programming. An application to matching biological sequences is given.
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Yiru Zhang, Changchang Cao, Hongde Liu et al. · 2014
Nucleosomes are the basic units of eukaryotic chromatin. The nucleosome positioning is dynamic for various cell types and biological states, resulting in specific gene regulation. Currently, there is no approach to find the correspondence between two sets of nucleosomes to reveal the difference of their positions. We develop a method for nucleosome positions alignment based on the dynamic programming algorithm, which can quantify the changes in nucleosome locations with scores and evaluate regional dynamics changes including translation and missing. Given the result of a peak list stands for nucleosome positions, to align the peaks from two samples, our method accumulate all pair scores for match, replacement or deletion and choose the maximum one as the optimal alignment. From nucleosome alignment we can find one-by-one correspondence between nucleosome positions in different cell stages and the conservative stable and variable regions, which can be used to recognize dynamic behaviors of nucleosome shift and eviction.
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Kui Zhang, Minghua Deng, Ting Chen et al. · Proceedings of the National Academy of Sciences · 2002
We develop a dynamic programming algorithm for haplotype block partitioning to minimize the number of representative single nucleotide polymorphisms (SNPs) required to account for most of the common haplotypes in each block. Any measure of haplotype quality can be used in the algorithm and of course the measure should depend on the specific application. The dynamic programming algorithm is applied to analyze the chromosome 21 haplotype data of Patil et al. [Patil, N., Berno, A. J., Hinds, D. A., Barrett, W. A., Doshi, J. M., Hacker, C. R., Kautzer, C. R., Lee, D. H., Marjoribanks, C., McDonough, D. P., et al. (2001) Science 294, 1719-1723], who searched for blocks of limited haplotype diversity. Using the same criteria as in Patil et al., we identify a total of 3,582 representative SNPs and 2,575 blocks that are 21.5% and 37.7% smaller, respectively, than those identified using a greedy algorithm of Patil et al. We also apply the dynamic programming algorithm to the same data set based on haplotype diversity. A total of 3,982 representative SNPs and 1,884 blocks are identified to account for 95% of the haplotype diversity in each block.
Cited by 341Open access
NTUR (臺灣機構典藏) · 1988
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Jonathan Lenchner · 2014
We study the following 1D and 1.5D problems: Given a set of m server locations {t1,...,tm}, lying along the x-axis and specified in increasing order, and n client locations {p1,..., pn}, also specified in increasing order along the
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Christian Bender, S. Meyer · Probability Uncertainty and Quantitative Risk · 2025
We introduce and analyze a family of linear least-squares Monte Carlo schemes for backward SDEs, which interpolate between the one-step dynamic programming scheme of Lemor, Warin, and Gobet (Bernoulli, 2006) and the multi-step dynamic programming scheme of Gobet and Turkedjiev (Mathematics of Computation, 2016). Our algorithm approximates conditional expectations over segments of the time grid. We discuss the optimal choice of the segment length depending on the ‘smoothness’ of the problem and show that, in typical situations, the complexity can be reduced compared to the state-of-the-art multi-step dynamic programming scheme.
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PO Ekoko · Global Journal of Mathematical Sciences · 2008
The stagecoach problem is a special type of network analysis problem in which the cities (nodes) are arranged in stages. By such human or natural arrangement, a journey from City 1 in stage 1 to City n in stage n involves visiting only one city in each intermediate stage. The stagecoach problem involves the determination of the minimum cost flow in which basically two methods have been in use. One of the methods is by listing all possible routes, computing their corresponding costs and selecting the minimum cost route. This procedure entails much computational effort. The second procedure is to determine the suboptimal cost of routes between any two consecutive stages. Though this second approach has less computational effort, its overall optimal policy could be wrong because the choice of a least expensive route at one stage may result in our adopting a more expensive route at a later stage. In this paper we present a recursive dynamic programming algorithm for solving the stagecoach problem. The algorithm is computationally more efficient than the first method as it obtains its minimum total cost using the suboptimal policies of the different stages without computing the cost of all the routes. By the dynamic programming algorithm of this paper, the possibility of missing the minimum cost route is ruled out as could happen in the second approach. The dynamic programming algorithm for obtaining the minimum cost path in a stagecoach problem is numerically illustrated Keywords: Network analysis, stagecoach, dynamic programming algorithmGlobal Journal of Mathematical Sciences Vol. 7 (1) 2008: pp. 15-20
Cited by 2Open access
Ou Wei, Bao‐Gang Sun · 2010
Vehicle Routing Problems (VRPs) are usually treated as static routing problem, since all the information is known before the routes been constructed. However, the real world VRPs are dynamic, where information relevant to the routing can change after the initial scheme has been done. This paper treats the dynamic VRPs with real time traffic information. We formulate the mathematic model of the VRPs, and propose a dynamic programming algorithm based on chaos optimization, whereas the route calculating module and dynamic programming module are introduced. Finally, the efficiency of the proposed algorithm is demonstrated by computer simulations.
Cited by 6
Li Zhidong, Xiaohuan Yu · Advances in intelligent systems research/Advances in Intelligent Systems Research · 2015
With the rapid development of computer technology, software engineering disciplines has developed rapidly not only in the aspect of theory, but is increasingly important in the practical application, and gradually formed methodologies, tools and management of three elements. In these three factors, the study of software project management is relatively backward, and even become a major obstacle in the development of software engineering problems. Therefore, in view of software engineering discipline facing the problem, this paper proposed dynamic programming algorithm should be applied to software engineering management.
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Jianguo Huang, Steven Kay · International Conference on Acoustics, Speech, and Signal Processing · 2003
A formalism for frequency estimation of multiple sinusoids in noise using dynamic programming is derived. A dynamic-programming-type algorithm for the frequency estimation of two close sinusoids in noise is proposed. Computer simulation results show an improvement over the periodogram.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cited by 1
Athanasios Kehagias, Pavlina Fragkou, Vassilios Petridis · 2003
In this paper we introduce a dynamic programming algorithm to perform linear text segmentation by global minimization of a segmentation cost function which consists of: (a) within-segment word similarity and (b) prior information about segment length. The evaluation of the segmentation accuracy of the algorithm on Choi's text collection showed that the algorithm achieves the best segmentation accuracy so far reported in the literature.
Cited by 24Open access
Zhu, Hong · Library and Archives Canada (Government of Canada) · 1998
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Hiroaki Sakoe, Seibi Chiba · IEEE Transactions on Acoustics Speech and Signal Processing · 1978
This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm superiority is established. A new technique, called slope constraint, is successfully introduced, in which the warping function slope is restricted so as to improve discrimination between words in different categories. The effective slope constraint characteristic is qualitatively analyzed, and the optimum slope constraint condition is determined through experiments. The optimized algorithm is then extensively subjected to experimental comparison with various DP-algorithms, previously applied to spoken word recognition by different research groups. The experiment shows that the present algorithm gives no more than about two-thirds errors, even compared to the best conventional algorithm.
Cited by 6,429