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Journal articles on the topic 'Linear search'

To see the other types of publications on this topic, follow the link: Linear search.

Author: Grafiati

Published: 25 May 2024

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1

Hohzaki, Ryusuke. "A SEARCH GAME TAKING ACCOUNT OF LINEAR EFFECTS AND LINEAR CONSTRAINTS OF SEARCHING RESOURCE." Journal of the Operations Research Society of Japan 55, no. 1 (2012): 1–22. http://dx.doi.org/10.15807/jorsj.55.1.

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2

Mäkinen, Erkki. "On linear search heuristics." Information Processing Letters 29, no. 1 (September 1988): 35–36. http://dx.doi.org/10.1016/0020-0190(88)90129-9.

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3

Hester, J. H., and D. S. Hirschberg. "Self-organizing linear search." ACM Computing Surveys 17, no. 3 (September 1985): 295–311. http://dx.doi.org/10.1145/5505.5507.

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4

Arora, Nitin, Garima Bhasin, and Neha Sharma. "Two way Linear Search Algorithm." International Journal of Computer Applications 107, no. 21 (December 18, 2014): 6–8. http://dx.doi.org/10.5120/19137-9622.

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5

Limoncelli, Thomas A. "10 optimizations on linear search." Communications of the ACM 59, no. 9 (August 24, 2016): 44–48. http://dx.doi.org/10.1145/2980976.

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6

Limoncelli, Thomas A. "10 Optimizations on Linear Search." Queue 14, no. 4 (August 2016): 20–33. http://dx.doi.org/10.1145/2984629.2984631.

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7

Korf, Richard E. "Linear-space best-first search." Artificial Intelligence 62, no. 1 (July 1993): 41–78. http://dx.doi.org/10.1016/0004-3702(93)90045-d.

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8

Foley, R. D., T. P. Hill, and M. C. Spruill. "Linear search with bounded resources." Naval Research Logistics 38, no. 4 (August 1991): 555–65. http://dx.doi.org/10.1002/1520-6750(199108)38:4<555::aid-nav3220380408>3.0.co;2-8.

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9

Kumari, Anchala. "Linear Search Versus Binary Search:A Statistical Comparison For Binomial Inputs." International Journal of Computer Science, Engineering and Applications 2, no. 2 (April 30, 2012): 29–39. http://dx.doi.org/10.5121/ijcsea.2012.2203.

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10

Balkhi, Zaid T. "THE GENERALIZED LINEAR SEARCH PROBLEM, EXISTENCE OF OPTIMAL SEARCH PATHS." Journal of the Operations Research Society of Japan 30, no. 4 (1987): 399–421. http://dx.doi.org/10.15807/jorsj.30.399.

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11

Manski, Charles F. "Optimal Search Profiling with Linear Deterrence." American Economic Review 95, no. 2 (April 1, 2005): 122–26. http://dx.doi.org/10.1257/000282805774669817.

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12

Heydari, Javad, Ali Tajer, and H. Vincent Poor. "Quickest Linear Search over Correlated Sequences." IEEE Transactions on Information Theory 62, no. 10 (October 2016): 5786–808. http://dx.doi.org/10.1109/tit.2016.2593772.

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13

Korytowski, A. "Inner search methods for linear programming." Applicationes Mathematicae 20, no. 2 (1988): 307–27. http://dx.doi.org/10.4064/am-20-2-307-327.

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14

Gorbunov, Dmitry S., and Viacheslav A. Ilyin. "Stoponium search at photon linear collider." Journal of High Energy Physics 2000, no. 11 (November 6, 2000): 011. http://dx.doi.org/10.1088/1126-6708/2000/11/011.

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15

Saha, Subir, Mridul K.Bhaumik, and Supratim Das. "A New Modified Linear Search Algorithm." International Journal of Mathematics Trends and Technology 65, no. 12 (December 25, 2019): 148–52. http://dx.doi.org/10.14445/22315373/ijmtt-v65i12p516.

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16

Beck, Anatole, and Micah Beck. "The linear search problem rides again." Israel Journal of Mathematics 53, no. 3 (December 1986): 365–72. http://dx.doi.org/10.1007/bf02786568.

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17

Rodler, Patrick. "Linear-Space Best-First Diagnosis Search." Proceedings of the International Symposium on Combinatorial Search 12, no. 1 (July 21, 2021): 188–90. http://dx.doi.org/10.1609/socs.v12i1.18579.

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Various model-based diagnosis scenarios require the computation of the most preferred fault explanations. Existing algorithms that are sound (i.e., output only actual fault explanations) and complete (i.e., can return all explanations), however, require exponential space to achieve this task. As a remedy, to enable successful diagnosis on memory-restricted devices and for memory-intensive problem cases, we propose RBF-HS, a diagnostic search based on Korf’s seminal RBFS algorithm. RBF-HS can enumerate an arbitrary ﬁxed number of fault explanations in best-ﬁrst order within linear space bounds, without sacriﬁcing the desirable soundness or completeness properties. Evaluations using real-world diagnosis cases show that RBF-HS, when used to compute minimum-cardinality fault explanations, in most cases saves substantial space (up to 98 %) while requiring only reasonably more or even less time than Reiter’s HS-Tree, one of the most widely used diagnostic algorithms with the same properties.

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18

Nickels, W., W. Rödder, L. Xu, and H. J. Zimmermann. "Intelligent gradient search in linear programming." European Journal of Operational Research 22, no. 3 (December 1985): 293–303. http://dx.doi.org/10.1016/0377-2217(85)90248-6.

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19

Zhang, Weixiong, and Richard E. Korf. "Performance of linear-space search algorithms." Artificial Intelligence 79, no. 2 (December 1995): 241–92. http://dx.doi.org/10.1016/0004-3702(94)00047-6.

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20

Baston, Vic, and Anatole Beck. "Generalizations in the linear search problem." Israel Journal of Mathematics 90, no. 1-3 (October 1995): 301–23. http://dx.doi.org/10.1007/bf02783218.

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21

Mejia, Carolina. "Linear secret sharing and the automatic search of linear rank inequalities." Applied Mathematical Sciences 9 (2015): 5305–24. http://dx.doi.org/10.12988/ams.2015.57478.

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22

Beck, Anatole, and Micah Beck. "The Revenge of the Linear Search Problem." SIAM Journal on Control and Optimization 30, no. 1 (January 1992): 112–22. http://dx.doi.org/10.1137/0330008.

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23

Gonzaga, Clovis C. "Search directions for interior linear-programming methods." Algorithmica 6, no. 1-6 (June 1991): 153–81. http://dx.doi.org/10.1007/bf01759039.

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24

Korf, Richard E. "Linear-time disk-based implicit graph search." Journal of the ACM 55, no. 6 (December 2008): 1–40. http://dx.doi.org/10.1145/1455248.1455250.

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25

Koehler, Gary J. "Linear Discriminant Functions Determined by Genetic Search." ORSA Journal on Computing 3, no. 4 (November 1991): 345–57. http://dx.doi.org/10.1287/ijoc.3.4.345.

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26

El-Rayes, A. B., Abd El-Moneim A. Mohamed, and Hamdy M. Abou Gabal. "LINEAR SEARCH FOR A BROWNIAN TARGET MOTION." Acta Mathematica Scientia 23, no. 3 (July 2003): 321–27. http://dx.doi.org/10.1016/s0252-9602(17)30338-7.

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27

Greenberg, Shlomo, and Daniel Kogan. "Linear search applied to global motion estimation." Multimedia Systems 12, no. 6 (November 22, 2006): 493–504. http://dx.doi.org/10.1007/s00530-006-0069-2.

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28

Németh, Károly, and Gustavo E. Scuseria. "Linear scaling density matrix search based onsignmatrices." Journal of Chemical Physics 113, no. 15 (October 15, 2000): 6035–41. http://dx.doi.org/10.1063/1.1308546.

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29

Hatem, Matthew, Roni Stern, and Wheeler Ruml. "Bounded Suboptimal Heuristic Search in Linear Space." Proceedings of the International Symposium on Combinatorial Search 4, no. 1 (August 20, 2021): 98–104. http://dx.doi.org/10.1609/socs.v4i1.18297.

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Abstract:

It is commonly appreciated that solving search problems optimally can overrun time and memory constraints. Bounded suboptimal search algorithms trade increased solution cost for reduced solving time and memory consumption. However, even suboptimal search can overrun memory on large problems. The conventional approach to this problem is to combine a weighted admissible heuristic with an optimal linear space algorithm, resulting in algorithms such as Weighted IDA* (wIDA*). However, wIDA* does not exploit distance-to-go estimates or inadmissible heuristics, which have recently been shown to be helpful for suboptimal search. In this paper, we present a linear space analogue of Explicit Estimation Search (EES), a recent algorithm specifically designed for bounded suboptimal search. We call our method Iterative Deepening EES (IDEES). In an empirical evaluation, we show that IDEES dramatically outperforms wIDA* on domains with non-uniform edge costs and can scale to problems that are out of reach for the original EES.

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30

Krivoi, S. L., and S. G. Raksha. "Search for invariant linear relationships in programs." Cybernetics 20, no. 6 (1985): 796–803. http://dx.doi.org/10.1007/bf01072165.

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31

Amin, Gholam R., and Ali Emrouznejad. "Optimizing search engines results using linear programming." Expert Systems with Applications 38, no. 9 (September 2011): 11534–37. http://dx.doi.org/10.1016/j.eswa.2011.03.030.

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32

P.Parmar, Vimal, and CK Kumbharana. "Comparing Linear Search and Binary Search Algorithms to Search an Element from a Linear List Implemented through Static Array, Dynamic Array and Linked List." International Journal of Computer Applications 121, no. 3 (July 18, 2015): 13–17. http://dx.doi.org/10.5120/21519-4495.

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33

Hatem, Matthew, and Wheeler Ruml. "Bounded Suboptimal Search in Linear Space: New Results." Proceedings of the International Symposium on Combinatorial Search 5, no. 1 (September 1, 2021): 89–96. http://dx.doi.org/10.1609/socs.v5i1.18327.

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Bounded suboptimal search algorithms are usually faster than optimal ones, but they can still run out of memory on large problems. This paper makes three contributions. First, we show how solution length estimates, used by the current state-of-the-art linear-space bounded suboptimal search algorithm Iterative Deepening EES, can be used to improve unbounded-space suboptimal search. Second, we convert one of these improved algorithms into a linear-space variant called Iterative Deepening A* epsilon, resulting in a new state of the art in linear-space bounded suboptimal search. Third, we show how Recursive Best-First Search can be used to create additional linear-space variants that have more stable performance. Taken together, these results significantly expand our armamentarium of bounded suboptimal search algorithms.

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34

Vighneshvel, T., and S. P. Arun. "Does linear separability really matter? Complex visual search is explained by simple search." Journal of Vision 13, no. 11 (September 12, 2013): 10. http://dx.doi.org/10.1167/13.11.10.

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35

Ahmed, Haffane, and Hasni Abdelhafid. "Cuckoo search optimization for linear antenna arrays synthesis." Serbian Journal of Electrical Engineering 10, no. 3 (2013): 371–80. http://dx.doi.org/10.2298/sjee130317010a.

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A recently developed metaheuristic optimization algorithm, the Cuckoo search algorithm, is used in this paper for the synthesis of symmetric uniformly spaced linear microstrip antennas array. Cuckoo search is based on the breeding strategy of Cuckoos augmented by a Levy flight behaviour found in the foraging habits of other species. This metaheuristic is tested on amplitude only pattern synthesis and amplitude and phase pattern synthesis. In both case, the objective, is to determinate the optimal excitations element that produce a synthesized radiation pattern within given bounds specified by a pattern mask.

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36

Ntzoufras, Ioannis, Jonathan J. Forster, and Petros Dellaportas. "Stochastic search variable selection for log-linear models." Journal of Statistical Computation and Simulation 68, no. 1 (December 2000): 23–37. http://dx.doi.org/10.1080/00949650008812054.

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37

Anashkina, N. V., and A. N. Shurupov. "Solving linear inequalities systems with local search algorithms." Prikladnaya diskretnaya matematika. Prilozhenie, no. 8 (December 1, 2015): 136–38. http://dx.doi.org/10.17223/2226308x/8/53.

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38

Ghosh, Subir. "Influential nonnegligible parameters under the search linear model." Communications in Statistics - Theory and Methods 16, no. 4 (January 1987): 1013–25. http://dx.doi.org/10.1080/03610928708829419.

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39

Tsai, S. M., and J. F. Yang. "Efficient algebraic code-excited linear predictive codebook search." IEE Proceedings - Vision, Image, and Signal Processing 153, no. 6 (2006): 761. http://dx.doi.org/10.1049/ip-vis:20060123.

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40

Mladenovic, Nenad, Dragan Urosevic, and Dionisio Pérez-Brito. "Variable neighborhood search for minimum linear arrangement problem." Yugoslav Journal of Operations Research 26, no. 1 (2016): 3–16. http://dx.doi.org/10.2298/yjor140928038m.

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The minimum linear arrangement problem is widely used and studied in many practical and theoretical applications. It consists of finding an embedding of the nodes of a graph on the line such that the sum of the resulting edge lengths is minimized. This problem is one among the classical NP-hard optimization problems and therefore there has been extensive research on exact and approximative algorithms. In this paper we present an implementation of a variable neighborhood search (VNS) for solving minimum linear arrangement problem. We use Skewed general VNS scheme that appeared to be successful in solving some recent optimization problems on graphs. Based on computational experiments, we argue that our approach is comparable with the state-of-the-art heuristic.

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41

Liu, P. L., B. J. Li, and Y. S. Trisno. "In search of a linear electrooptic amplitude modulator." IEEE Photonics Technology Letters 3, no. 2 (February 1991): 144–46. http://dx.doi.org/10.1109/68.76869.

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42

Kong, Garry, David Alais, and Erik Van der Burg. "Investigating Linear Separability in Visual Search for Orientation." Journal of Vision 16, no. 12 (September 1, 2016): 1280. http://dx.doi.org/10.1167/16.12.1280.

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43

Pryde, A. J., and R. M. Phatarfod. "Multiplicities of eigenvalues of some linear search schemes." Linear Algebra and its Applications 291, no. 1-3 (April 1999): 115–24. http://dx.doi.org/10.1016/s0024-3795(98)10246-x.

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44

Lòpez, Pablo, Ernesto Pimentel, Joshua S. Hodas, Jeffrey Polakow, and Lubomira Stoilova. "Isolating Resource Consumption in Linear Logic Proof Search." Electronic Notes in Theoretical Computer Science 70, no. 2 (December 2002): 1–10. http://dx.doi.org/10.1016/s1571-0661(04)80502-4.

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45

Esmaeili, M., A. Alampour, and T. A. Gulliver. "Decoding Binary Linear Block Codes Using Local Search." IEEE Transactions on Communications 61, no. 6 (June 2013): 2138–45. http://dx.doi.org/10.1109/tcomm.2013.041113.120057.

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46

Cervesato, Iliano, Joshua S. Hodas, and Frank Pfenning. "Efficient resource management for linear logic proof search." Theoretical Computer Science 232, no. 1-2 (February 2000): 133–63. http://dx.doi.org/10.1016/s0304-3975(99)00173-5.

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47

Manber, Udi. "Recognizing breadth-first search trees in linear time." Information Processing Letters 34, no. 4 (April 1990): 167–71. http://dx.doi.org/10.1016/0020-0190(90)90155-q.

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48

Luh, Hsing, and Ray Tsaih. "An efficient search direction for linear programming problems." Computers & Operations Research 29, no. 2 (February 2002): 195–203. http://dx.doi.org/10.1016/s0305-0548(00)00069-1.

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49

Velez, German Correa, Fernando Mesa, and Pedro Pablo Cardenas Alzate. "Linear search optimization through the Armijo rule method." Contemporary Engineering Sciences 11, no. 16 (2018): 771–78. http://dx.doi.org/10.12988/ces.2018.8121.

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50

Boldyrev, A. I., and J. Simons. "Theoretical search for small linear doubly charged anions." Journal of Chemical Physics 98, no. 6 (March 15, 1993): 4745–52. http://dx.doi.org/10.1063/1.464978.

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