Y Shao, J. E. W. Mayhew · 1996
We describe an edge-based stereo correspondence algorithm in the model-driven vision system. A constraint derived from the 3D model is used to prune false alarms and speed up the matching process. This constraint is based on computional considerations and experimental and psychological observations concerning vertical disparities. An edge constraint is also presented. A numbering scheme is used to facilitate the implementation of the correspondence algorithm using dynamic programming. Experiments on natural images show that the corre-spondence of an edge can usually be achieved in a few seconds. The computed disparities are further incorporated into object model to re-ne the estimates of object pose.
Cited by 1
George A. Komatsoulis, Michael S. Waterman · 1997
A new nearest-neighbor method for detecting chimeric 16S rRNA artifacts generated during PCR amplification from mixed populations has been developed. The method uses dynamic programming to generate an optimal chimeric alignment, defined as the highest scoring alignment between a query and a concatenation of a 5' and a 3' segment from two separate entries from a database of related sequences. Chimeras are detected by studying the scores and form of the chimeric and global sequence alignments. The chimeric alignment method was found to be marginally more effective than k-tuple based nearest-neighbor methods in simulation studies, but its most effective use is in concert with k-tuple methods.
Cited by 0
Carlos Eduardo Ferreira, Alexandre Freire, G.A. Puglia · Electronic Notes in Discrete Mathematics · 2011
Cited by 1Open access
Shipra Agrawal, Zizhuo Wang, Yinyu Ye · arXiv (Cornell University) · 2009
A natural optimization model that formulates many online resource allocation and revenue management problems is the online linear program (LP) in which the constraint matrix is revealed column by column along with the corresponding objective coefficient. In such a model, a decision variable has to be set each time a column is revealed without observing the future inputs and the goal is to maximize the overall objective function. In this paper, we provide a near-optimal algorithm for this general class of online problems under the assumption of random order of arrival and some mild conditions on the size of the LP right-hand-side input. Specifically, our learning-based algorithm works by dynamically updating a threshold price vector at geometric time intervals, where the dual prices learned from the revealed columns in the previous period are used to determine the sequential decisions in the current period. Due to the feature of dynamic learning, the competitiveness of our algorithm improves over the past study of the same problem. We also present a worst-case example showing that the performance of our algorithm is near-optimal.
Cited by 36Open access
G. Self · NASA Technical Reports Server (NASA) · 1966
Cited by 0
Clémentin Tayou Djamegni, Maurice Tchuenté · Parallel Processing Letters · 2000
Given the real entries w(i, j), 1≤ i < j ≤n, and c(i, i+1), 1≤ i< n, the dynamic programming problem studied here consists of computing the coefficients c ( i , j )= w ( i , j ) + min i < k < j { c ( i , k )+ c ( k , j )}, for 1≤ i< j≤n. We show that this problem can be solved in optimal time T*=2n-1 on a regular array of size n 2 /10+ O ( n ). The number of processors required by this new algorithm improves the best previously known bound n 2 /10+ O ( n ). The idea introduced here is to apply to the dependence graph associated with the problem a pre-processing that removes long-range communications in order to obtain a new locally connected graph which is suitable for the application of powerful allocation techniques.
Cited by 1
Jian Zheng, Yuhan Chen · 2015
Based on the fact that target emerged by ground clutter or noise problem usually results in a low SNR or SCR and degrade the detecting ability, a dynamic programming algorithm and track before detect (TBD) algorithm is proposed to detecting the weak target. By analysis the target moving modal, the algorithm revised the target transporting state consulting on the target Morphological characteristic. The simulation and experiment result have approved that the proposed method have high detecting ability on weak target detecting from radar echo.
Cited by 0Open access
Alessandro Alla, Maurizio Falcone, Dante Kalise · arXiv (Cornell University) · 2013
We present an accelerated algorithm for the solution of static Hamilton-Jacobi-Bellman equations related to optimal control problems. Our scheme is based on a classic policy iteration procedure, which is known to have superlinear convergence in many relevant cases provided the initial guess is sufficiently close to the solution. In many cases, this limitation degenerates into a behavior similar to a value iteration method, with an increased computation time. The new scheme circumvents this problem by combining the advantages of both algorithms with an efficient coupling. The method starts with a value iteration phase and then switches to a policy iteration procedure when a certain error threshold is reached. A delicate point is to determine this threshold in order to avoid cumbersome computation with the value iteration and, at the same time, to be reasonably sure that the policy iteration method will finally converge to the optimal solution. We analyze the methods and efficient coupling in a number of examples in dimension two, three and four illustrating its properties.
Cited by 0Open access
Shipra Agrawal, Zizhuo Wang, Yinyu Ye · Operations Research · 2014
A natural optimization model that formulates many online resource allocation problems is the online linear programming (LP) problem in which the constraint matrix is revealed column by column along with the corresponding objective coefficient. In such a model, a decision variable has to be set each time a column is revealed without observing the future inputs, and the goal is to maximize the overall objective function. In this paper, we propose a near-optimal algorithm for this general class of online problems under the assumptions of random order of arrival and some mild conditions on the size of the LP right-hand-side input. Specifically, our learning-based algorithm works by dynamically updating a threshold price vector at geometric time intervals, where the dual prices learned from the revealed columns in the previous period are used to determine the sequential decisions in the current period. Through dynamic learning, the competitiveness of our algorithm improves over the past study of the same problem. We also present a worst case example showing that the performance of our algorithm is near optimal.
Cited by 253
Александр Владимирович Докучаев, Alexandr Vladimirovich Dokuchaev · SHILAP Revista de lepidopterología · 2008
Article proposes algorithms for solution of stochastic problems of dynamic programming of the big dimensions. The problem of distribution of resources is studied.
Cited by 1Open access
Fulmański, Piotr, Nowakowski, Andrzej, Pustelnik, Jan · Jagiellonian University Repository (Jagiellonian University) · 2015
Cited by 0
W. Harrold, NG Kingsbury · Cambridge University Engineering Department Publications Database · 1987
A dynamic programming algorithm for joint data detection and carrier phase estimation of continuous-phase-modulated signal is presented. The intent is to combine the robustness of noncoherent detectors with the superior performance of coherent ones. The algorithm differs from the Viterbi algorithm only in the metric that it maximizes over the possible transmitted data sequences. This metric is influenced both by the correlation with the received signal and the current estimate of the carrier phase. Carrier-phase estimation is based on decision guiding, but there is no external phase-locked loop. Instead, the phase of the best complex correlation with the received signal over the last few signaling intervals is used. The algorithm is slightly more complex than the coherent Viterbi algorithm but does not require narrowband filtering of the recovered carrier, as earlier appproaches did, to achieve the same level of performance.
Cited by 3
Ro Alla, Maurizio Falcone, Dante Kalise et al. · 2015
Abstract. We present an accelerated algorithm for the solution of static Hamilton-Jacobi-Bellman equations related to optimal control problems. Our scheme is based on a classic policy iteration procedure, which is known to have superlinear convergence in many relevant cases pro-vided the initial guess is sufficiently close to the solution. This limitation often degenerates into a behavior similar to a value iteration method, with an increased computation time. The new scheme circumvents this problem by combining the advantages of both algorithms with an efficient coupling. The method starts with a coarse-mesh value iteration phase and then switches to a fine-mesh policy iteration procedure when a certain error threshold is reached. A delicate point is to determine this threshold in order to avoid cumbersome computations with the value iteration and at the same time, to ensure the convergence of the policy iteration method to the optimal solution. We analyze the methods and efficient coupling in a number of examples in different dimensions, illustrating their properties. Key words. policy iteration, dynamic programming, semi-Lagrangian schemes, Hamilton-Jacobi equations, optimal control
Cited by 0
Akito Taneda, Kiyoshi Asai · Computational and Structural Biotechnology Journal · 2020
Codon optimization in protein-coding sequences (CDSs) is a widely used technique to promote the heterologous expression of target genes. In codon optimization, a combinatorial space of nucleotide sequences that code a given amino acid sequence and take into account user-prescribed forbidden sequence motifs is explored to optimize multiple criteria. Although evolutionary algorithms have been used to tackle such complex codon optimization problems, evolutionary codon optimization tools do not provide guarantees to find the optimal solutions for these multicriteria codon optimization problems. We have developed a novel multicriteria dynamic programming algorithm, COSMO. By using this algorithm, we can obtain all Pareto-optimal solutions for the multiple features of CDS, which include codon usage, codon context, and the number of hidden stop codons. User-prescribed forbidden sequence motifs are rigorously excluded from the Pareto-optimal solutions. To accelerate CDS design by COSMO, we introduced constraints that reduce the number of Pareto-optimal solutions to be processed in a branch-and-bound manner. We benchmarked COSMO for run-time and the number of generated solutions by adapting selected human genes to yeast codon usage frequencies, and found that the constraints effectively reduce the run-time. In addition to the benchmarking of COSMO, a multi-objective genetic algorithm (MOGA) for CDS design was also benchmarked for the same two aspects and their performances were compared. In this comparison, (i) MOGA identified significantly fewer Pareto-optimal solutions than COSMO, and (ii) the MOGA solutions did not achieve the same mean hypervolume values as those provided by COSMO. These results suggest that generating the whole set of the Pareto-optimal solutions of the codon optimization problems is a difficult task for MOGA.
Cited by 22Open access
T. Gandhi, Mau‐Tsuen Yang, Rangachar Kasturi et al. · IEEE Transactions on Image Processing · 2006
A computer vision-based system using images from an airborne aircraft can increase flight safety by aiding the pilot to detect obstacles in the flight path so as to avoid mid-air collisions. Such a system fits naturally with the development of an external vision system proposed by NASA for use in high-speed civil transport aircraft with limited cockpit visibility. The detection techniques should provide high detection probability for obstacles that can vary from subpixels to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter. Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. For this purpose, we have implemented a number of algorithms to detect airborne obstacles using image sequences obtained from a camera mounted on an aircraft. This paper describes the methodology used for characterizing the performance of the dynamic programming obstacle detection algorithm and its special cases. The experimental results were obtained using several types of image sequences, with simulated and real backgrounds. The approximate performance of the algorithm is also theoretically derived using principles of statistical analysis in terms of the signal-to-noise ration (SNR) required for the probabilities of false alarms and misdetections to be lower than prespecified values. The theoretical and experimental performance are compared in terms of the required SNR.
Cited by 24
Lisha He, Liangjing Mao, Lijun Xie · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Infrared (IR) target detection is a key part of airborne infrared weapon system, especially the detection of poor dim
moving IR target embedded in complex context. This paper presents an improved Dynamic Programming (DP)
algorithm in allusion to low Signal to Noise Ratio (SNR) infrared dim moving targets under cluttered context. The
algorithm brings the dim target to prominence by accumulating the energy of pixels in the image sequence, after
suppressing the background noise with a mathematical morphology preprocessor. As considering the continuity and
stabilization of target's energy and forward direction, this algorithm has well solved the energy scattering problem that
exists in the original DP algorithm. An effective energy segmentation threshold is given by a Contrast-Limited Adaptive
Histogram Equalization (CLAHE) filter with a regional peak extraction algorithm. Simulation results show that the
improved DP tracking algorithm performs well in detecting poor dim targets.
Cited by 1
Peter Voigt Knudsen, Jan Madsen · 2002
This paper presents the PACE partitioning algorithm which is used in the LYCOS co-synthesis system for partitioning control/dataflow graphs into hardware and software parts. The algorithm is a dynamic programming algorithm which solves both the problem of minimizing system execution time with a hardware area constraint and the problem of minimizing hardware area with a system execution time constraint. The target architecture consists of a single microprocessor and a single hardware chip (ASIC, FPGA, etc.) which are connected by a communication channel. The algorithm incorporates a realistic communication model and thus attempts to minimize communication overhead. The time-complexity of the algorithm is O(n/sup 2//spl middot//spl Ascr/) and the space-complexity is O(n/spl middot//spl Ascr/) where /spl Ascr/ is the total area of the hardware chip and n the number of code fragments which may be placed in either hardware or software.
Cited by 51
Yang,M.T., Kasturi,R., Gandhi,T. et al. · 2006
Cited by 0
Yifei Wang, SuRong Wang · Journal of Physics Conference Series · 2020
Abstract This paper describes the problem of intelligent paper grouping and its mathematical model. By optimizing and improving the traditional dynamic programming algorithm, its space complexity is reduced from O(nb) to O(b). At the same time, the flexibility of dynamic programming algorithm is increased by using marker function and tracking algorithm, and the result composition is tracked to obtain the optimal solution. Finally, through several experiments, the improved dynamic programming algorithm is compared with the greedy algorithm and brute force algorithm, and it is found that the improved dynamic programming algorithm has a very good result and is with high efficiency when applied to the simple test paper. It is the most recommended algorithm among the two algorithms compared in this paper.
Cited by 1Open access
Fernando S. Oliveira · INFORMS journal on computing · 2007
We extend Lemke's algorithm to solve a dynamic pricing problem. We identify an instance in which Lemke's algorithm fails to converge to an optimal solution (when an optimum does exist) and propose a constraint logic programming solution to this problem. We analyze the complexity of the extended Lemke's algorithm. Our analysis shows that, in the short term, dynamic pricing can be used to improve resource management efficiency. It is also shown that dynamic pricing can be used to manage the long-term behavior of demand.
Cited by 7Open access