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Journal articles on the topic 'Optimality'

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

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Published: 4 June 2021

Last updated: 8 June 2024

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1

Papadakis, Ioannis N. M. "On the Universal Encoding Optimality of Primes." Mathematics 9, no. 24 (December 7, 2021): 3155. http://dx.doi.org/10.3390/math9243155.

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The factorial-additive optimality of primes, i.e., that the sum of prime factors is always minimum, implies that prime numbers are a solution to an integer linear programming (ILP) encoding optimization problem. The summative optimality of primes follows from Goldbach’s conjecture, and is viewed as an upper efficiency limit for encoding any integer with the fewest possible additions. A consequence of the above is that primes optimally encode—multiplicatively and additively—all integers. Thus, the set P of primes is the unique, irreducible subset of ℤ—in cardinality and values—that optimally encodes all numbers in ℤ, in a factorial and summative sense. Based on these dual irreducibility/optimality properties of P, we conclude that primes are characterized by a universal “quantum type” encoding optimality that also extends to non-integers.

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2

van de Vijver, Ruben. "Optimality theory." Lingua 110, no. 11 (November 2000): 881–90. http://dx.doi.org/10.1016/s0024-3841(00)00012-7.

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3

Dayan, Peter, and Jon Oberlander. "Vaulting optimality." Behavioral and Brain Sciences 14, no. 2 (June 1991): 221–22. http://dx.doi.org/10.1017/s0140525x00066231.

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4

Dong, Lixin, Haixing Zhao, and Hong-Jian Lai. "Local Optimality of Mixed Reliability for Several Classes of Networks with Fixed Sizes." Axioms 11, no. 3 (February 24, 2022): 91. http://dx.doi.org/10.3390/axioms11030091.

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In this work, the local optimality of mixed reliability of networks is surveyed. A reliability comparison criterion related with subgraphs to measure the local optimality of the mixed reliability of networks is established. Using the comparison criterion, we characterize all locally optimally mixed reliable networks among all n-vertex networks with fixed sizes m=n, m=n+1 and m=n+2, respectively.

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5

Bonifacius, Lucas, and Karl Kunisch. "Time-optimality by distance-optimality for parabolic control systems." ESAIM: Mathematical Modelling and Numerical Analysis 54, no. 1 (January 2020): 79–103. http://dx.doi.org/10.1051/m2an/2019046.

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The equivalence of time-optimal and distance-optimal control problems is shown for a class of parabolic control systems. Based on this equivalence, an approach for the efficient algorithmic solution of time-optimal control problems is investigated. Numerical examples are provided to illustrate that the approach works well in practice.

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6

Goldenberg, Meir, Ariel Felner, Nathan Sturtevant, Robert C. Holte, and Jonathan Schaeffer. "Optimal-Generation Variants of EPEA*." Proceedings of the International Symposium on Combinatorial Search 4, no. 1 (August 20, 2021): 89–97. http://dx.doi.org/10.1609/socs.v4i1.18288.

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It is known that A* is optimal with respect to the expanded nodes (Dechter and Pearl 1985) (D&P). The exact meaning of this optimality varies depending on the class of algorithms and instances over which A* is claimed to be optimal. A* does not provide any optimality guarantees with respect to the generated nodes. However, such guarantees may be critical for optimally solving instances of domains with a large branching factor. In this paper, we introduce two new variants of the recently introduced Enhanced Partial Expansion A* algorithm (EPEA*) (Felner et al. 2012). We leverage the results of D&P to show that these variants possess optimality with respect to the generated nodes in much the same sense as A* possesses optimality with respect to the expanded nodes. The results in this paper are theoretical. A study of the practical performance of the new variants is beyond the scope of this paper.

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7

Blutner, Reinhard, and Henk Zeevat. "Optimality-theoretic pragmatics." ZAS Papers in Linguistics 51 (January 1, 2009): 1–25. http://dx.doi.org/10.21248/zaspil.51.2009.372.

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The article aims to give an overview about the application of Optimality Theory (OT) to the domain of pragmatics. In the introductory part we discuss different ways to view the division of labor between semantics and pragmatics. Rejecting the doctrine of literal meaning we conform to (i) semantic underdetermination and (ii) contextualism (the idea that the mechanism of pragmatic interpretation is crucial both for determining what the speaker says and what he means). Taking the assumptions (i) and (ii) as essential requisites for a natural theory of pragmatic interpretation, section 2 introduces the three main views conforming to these assumptions: Relevance theory, Levinson’s theory of presumptive meanings, and the Neo-Gricean approach. In section 3 we explain the general paradigm of OT and the idea of bidirectional optimization. We show how the idea of optimal interpretation can be used to restructure the core ideas of these three different approaches. Further, we argue that bidirectional OT has the potential to account both for the synchronic and the diachronic perspective on pragmatic interpretation. Section 4 lists relevant examples of using the framework of bidirectional optimization in the domain of pragmatics. Section 5 provides some general conclusions. Modeling both for the synchronic and the diachronic perspective on pragmatics opens the way for a deeper understanding of the idea of naturalization and (cultural) embodiment in the context of natural language interpretation.

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8

Radner, Daisie, and Michael Radner. "Optimality in Biology." Monist 81, no. 4 (1998): 669–86. http://dx.doi.org/10.5840/monist199881433.

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9

Huberman, Gur. "Optimality of Periodicity." Review of Economic Studies 55, no. 1 (January 1988): 127. http://dx.doi.org/10.2307/2297533.

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10

Hu, Feifang, and William F. Rosenberger. "Optimality, Variability, Power." Journal of the American Statistical Association 98, no. 463 (September 2003): 671–78. http://dx.doi.org/10.1198/016214503000000576.

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11

Fisher, Gregg S., Philip Z. Maymin, and Zakhar G. Maymin. "Risk Parity Optimality." Journal of Portfolio Management 41, no. 2 (January 31, 2015): 42–56. http://dx.doi.org/10.3905/jpm.2015.41.2.042.

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12

IGNIZIO, JAMES P. "ILLUSIONS OF OPTIMALITY." Engineering Optimization 31, no. 6 (August 1999): 749–61. http://dx.doi.org/10.1080/03052159908941395.

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13

Archangeli, D. B. "Introducing Optimality Theory." Annual Review of Anthropology 28, no. 1 (October 1999): 531–52. http://dx.doi.org/10.1146/annurev.anthro.28.1.531.

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14

Demaine, Erik D., Dion Harmon, John Iacono, and Mihai Ptraşcu. "Dynamic Optimality—Almost." SIAM Journal on Computing 37, no. 1 (January 2007): 240–51. http://dx.doi.org/10.1137/s0097539705447347.

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15

Lawall, Julia L., and Harry G. Mairson. "Optimality and inefficiency." ACM SIGPLAN Notices 31, no. 6 (June 15, 1996): 92–101. http://dx.doi.org/10.1145/232629.232639.

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16

Cabanac, Michel. "Criteria for optimality." Behavioral and Brain Sciences 14, no. 2 (June 1991): 218. http://dx.doi.org/10.1017/s0140525x0006619x.

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17

Helweg, David A., and Herbert L. Roitblat. "Optimality and constraint." Behavioral and Brain Sciences 14, no. 2 (June 1991): 222–23. http://dx.doi.org/10.1017/s0140525x00066243.

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18

Miller, Dauglas A., and Steven W. Zucker. "Complexity and optimality." Behavioral and Brain Sciences 14, no. 2 (June 1991): 227–28. http://dx.doi.org/10.1017/s0140525x00066309.

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19

Geanakoplos, J. D., and H. M. Polemarchakis. "Observability and optimality." Journal of Mathematical Economics 19, no. 1-2 (January 1990): 153–65. http://dx.doi.org/10.1016/0304-4068(90)90040-g.

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20

Wasilkowski, G. W. "Average case optimality." Journal of Complexity 1, no. 1 (October 1985): 107–17. http://dx.doi.org/10.1016/0885-064x(85)90023-8.

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21

Byron, Michael. "Satisficing and Optimality." Ethics 109, no. 1 (October 1998): 67–93. http://dx.doi.org/10.1086/233874.

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22

Iacono, John. "Key-Independent Optimality." Algorithmica 42, no. 1 (February 7, 2005): 3–10. http://dx.doi.org/10.1007/s00453-004-1136-8.

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23

Ghosal, S., and H. M. Polemarchakis. "Exchange and optimality." Economic Theory 13, no. 3 (April 19, 1999): 629–42. http://dx.doi.org/10.1007/s001990050273.

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24

Horan, Barbara L. "What price optimality?" Biology & Philosophy 7, no. 1 (January 1992): 89–109. http://dx.doi.org/10.1007/bf00130167.

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25

Osmolovskii, Nikolai P., Meizhi Qian, and Jan Sokołowski. "Network optimality conditions." Control and Cybernetics 52, no. 2 (June 1, 2023): 129–80. http://dx.doi.org/10.2478/candc-2023-0035.

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Abstract Optimality conditions for optimal control problems arising in network modeling are discussed. We confine ourselves to the steady state network models. Therefore, we consider only control systems described by ordinary differential equations. First, we derive optimality conditions for the nonlinear problem for a single beam. These conditions are formulated in terms of the local Pontryagin maximum principle and the matrix Riccati equation. Then, the optimality conditions for the control problem for networks posed on an arbitrary planar graph are discussed. This problem has a set of independent variables x i varying within their intervals [0, l i], associated with the corresponding beams at network edges. The lengths l i of intervals are not specified and must be determined. So, the optimization problem is non-standard, it is a combination of control and design of networks. However, using a linear change of the independent variables, it can be reduced to a standard one, and we show this. Two simple numerical examples for the single-beam problem are considered.

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26

Tian, Chao, Jun Chen, Suhas N. Diggavi, and Shlomo Shamai. "Optimality and Approximate Optimality of Source-Channel Separation in Networks." IEEE Transactions on Information Theory 60, no. 2 (February 2014): 904–18. http://dx.doi.org/10.1109/tit.2013.2291787.

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27

Mann, Elke. "Optimality equations and sensitive optimality in bounded Markov decision processes1." Optimization 16, no. 5 (January 1985): 767–81. http://dx.doi.org/10.1080/02331938508843074.

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28

Das, Ashish, and A. Dey. "Universal optimality and non-optimality of some row-column designs." Journal of Statistical Planning and Inference 31, no. 2 (May 1992): 263–71. http://dx.doi.org/10.1016/0378-3758(92)90035-q.

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29

Blum, Chawla, and Kalai. "Static Optimality and Dynamic Search-Optimality in Lists and Trees." Algorithmica 36, no. 3 (July 2003): 249–60. http://dx.doi.org/10.1007/s00453-003-1015-8.

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30

Giménez, Eduardo L., and Miguel Rodríguez. "Optimality of Relaxing Revenue-neutral Restrictions in." Revista Hacienda Pública Española 233, no. 2 (June 2020): 3–24. http://dx.doi.org/10.7866/hpe-rpe.20.2.1.

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31

Miceikienė, Astrida, and Erika Besusparienė. "OPTIMALIOS ŪKININKŲ MOKESČIŲ SISTEMOS FORMAVIMAS." Management Theory and Studies for Rural Business and Infrastructure Development 38, no. 3 (September 29, 2016): 261–72. http://dx.doi.org/10.15544/mts.2016.21.

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Išsamių tyrimų apie optimalią ūkininkų ūkių mokesčių sistemą, skatinančią ūkių gyvybingumą, konkurencingumą ir tvarumą, nėra Problema – kokia yra optimali ūkininkų ūkių mokesčių sistema ir kokie turi būti taikomi principai ir kriterijai optimaliai ūkininkų ūkių mokesčių sistemai nustatyti. Tyrimo tikslas – atlikus optimalios mokesčių sistemos teorinę analizę, patikslinus optimalios mokesčių sistemos sąvoką bei identifikavus šios sistemos vystymosi kryptis ir ūkininkų ūkių apmokestinimo problemas, parengti optimalios ūkininkų ūkių mokesčių sistemos teorinį modelį. Egzistuojanti ūkininkų ūkių mokesčių sistema nėra optimali, nes neužtikrinamas maksimalus valstybės pajamų lygis dėl moralinės žalos poveikio mokesčių sistemai, neužtikrinamos ūkininkų gerovės funkcijos, nepakankamos investicijos į žmogiškąjį kapitalą bei mažai sprendžiamos aplinkosauginės problemos.

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32

Miyamoto, Keisuke, and Keiichiro Yasuda. "Proximate Optimality Principle Based Tabu Search." Proceedings of OPTIS 2004.6 (2004): 67–72. http://dx.doi.org/10.1299/jsmeoptis.2004.6.67.

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33

Yamamoto, H., and H. Yokoo. "Average-sense optimality and competitive optimality for almost instantaneous VF codes." IEEE Transactions on Information Theory 47, no. 6 (2001): 2174–84. http://dx.doi.org/10.1109/18.945241.

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34

Huang, X. X. "OPTIMALITY CONDITIONS AND APPROXIMATE OPTIMALITY CONDITIONS IN LOCALLY LIPSCHITZ VECTOR OPTIMIZATION." Optimization 51, no. 2 (April 2002): 309–21. http://dx.doi.org/10.1080/02331930290019440.

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35

Perry, Amelia, Alexander S. Wein, Afonso S. Bandeira, and Ankur Moitra. "Optimality and sub-optimality of PCA I: Spiked random matrix models." Annals of Statistics 46, no. 5 (October 2018): 2416–51. http://dx.doi.org/10.1214/17-aos1625.

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36

Chakraborty, Uttam. "Optimality Theory in Bangla Phonology: Introducing Specific Constraints." Paripex - Indian Journal Of Research 3, no. 5 (January 15, 2012): 131–32. http://dx.doi.org/10.15373/22501991/may2014/43.

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37

Avakov, E. R., and G. G. Magaril-Il’yaev. "Local controllability and optimality." Sbornik: Mathematics 212, no. 7 (July 1, 2021): 887–920. http://dx.doi.org/10.1070/sm9434.

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38

Zeleny, Milan. "Rethinking optimality: eight concepts." Human Systems Management 15, no. 1 (1996): 1–4. http://dx.doi.org/10.3233/hsm-1996-15101.

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39

Gillen, Sarah L., Joseph A. Waldron, and Martin Bushell. "Codon optimality in cancer." Oncogene 40, no. 45 (September 28, 2021): 6309–20. http://dx.doi.org/10.1038/s41388-021-02022-x.

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AbstractA key characteristic of cancer cells is their increased proliferative capacity, which requires elevated levels of protein synthesis. The process of protein synthesis involves the translation of codons within the mRNA coding sequence into a string of amino acids to form a polypeptide chain. As most amino acids are encoded by multiple codons, the nucleotide sequence of a coding region can vary dramatically without altering the polypeptide sequence of the encoded protein. Although mutations that do not alter the final amino acid sequence are often thought of as silent/synonymous, these can still have dramatic effects on protein output. Because each codon has a distinct translation elongation rate and can differentially impact mRNA stability, each codon has a different degree of ‘optimality’ for protein synthesis. Recent data demonstrates that the codon preference of a transcriptome matches the abundance of tRNAs within the cell and that this supply and demand between tRNAs and mRNAs varies between different cell types. The largest observed distinction is between mRNAs encoding proteins associated with proliferation or differentiation. Nevertheless, precisely how codon optimality and tRNA expression levels regulate cell fate decisions and their role in malignancy is not fully understood. This review describes the current mechanistic understanding on codon optimality, its role in malignancy and discusses the potential to target codon optimality therapeutically in the context of cancer.

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40

Foley, Robert. "Optimality Theory in Anthropology." Man 20, no. 2 (June 1985): 222. http://dx.doi.org/10.2307/2802382.

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41

Lee, Jae-Young. "Revisiting Classic Optimality Theory." Journal of Modern British and American Language and Literature 33, no. 1 (February 28, 2015): 1. http://dx.doi.org/10.21084/jmball.2015.02.33.1.1.

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42

Svensson, Lars-Gunnar. "σ-Optimality and Fairness." International Economic Review 35, no. 2 (May 1994): 527. http://dx.doi.org/10.2307/2527068.

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43

Seton, Francis, A. I. Katsenelinboigen, S. M. Movsovich, and Iu V. Ovsienko. "Basic Economics and Optimality." Economic Journal 99, no. 396 (June 1989): 508. http://dx.doi.org/10.2307/2234057.

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44

Das, Ashish, and A. Dey. "Optimality of RowColumn Designs." Calcutta Statistical Association Bulletin 39, no. 1-2 (March 1990): 63–72. http://dx.doi.org/10.1177/0008068319900107.

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45

Cambini, A., and L. Martein. "Second order optimality conditions." Journal of Discrete Mathematical Sciences and Cryptography 3, no. 1-3 (April 2000): 243–52. http://dx.doi.org/10.1080/09720529.2000.10697912.

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46

Schlee, Edward E. "Surplus Maximization and Optimality." American Economic Review 103, no. 6 (October 1, 2013): 2585–611. http://dx.doi.org/10.1257/aer.103.6.2585.

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Expected consumer's surplus rarely represents preferences over price lotteries. Still, I give sufficient conditions for policies which maximize aggregate expected surplus to be interim Pareto Optimal. Besides two standard partial equilibrium conditions, I assume that feasible prices satisfy a single-crossing property; and each consumer's indirect utility satisfies increasing differences in the price and income. I use the result to extend well-known welfare conclusions beyond the knife-edge quasilinear utility case. Since increasing differences puts no upper bound on risk aversion, the result is useful for applications in which risk aversion is important. (JEL D11, D24, D42, D81, D83, L42)

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47

Tesar, Bruce, and Paul Smolensky. "Learnability in Optimality Theory." Linguistic Inquiry 29, no. 2 (April 1998): 229–68. http://dx.doi.org/10.1162/002438998553734.

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In this article we show how Optimality Theory yields a highly general Constraint Demotion principle for grammar learning. The resulting learning procedure specifically exploits the grammatical structure of Optimality Theory, independent of the content of substantive constraints defining any given grammatical module. We decompose the learning problem and present formal results for a central subproblem, deducing the constraint ranking particular to a target language, given structural descriptions of positive examples. The structure imposed on the space of possible grammars by Optimality Theory allows efficient convergence to a correct grammar. We discuss implications for learning from overt data only, as well as other learning issues. We argue that Optimality Theory promotes confluence of the demands of more effective learnability and deeper linguistic explanation.

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48

Gáspár, Miklós. "Coordination in Optimality Theory." Nordic Journal of Linguistics 22, no. 2 (December 1999): 157–81. http://dx.doi.org/10.1080/03325860050179236.

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The paper offers an account of coordination within the framework of Optimality Theory, which makes use of violable and ranked constraints. Coordination is explained with the help of nine constraints, seven of which are needed in the theory independently of coordination, while the remaining two are coordination-specific constraints. Related phenomena of Unbalanced Coordination and Extraordinary Balanced Coordination are also discussed and differences among Norwegian, English and Hungarian are explained by the difference in the relative ranking of the four relevant constraints.

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49

Laumond, Jean-Paul, Nicolas Mansard, and Jean-Bernard Lasserre. "Optimality in robot motion." Communications of the ACM 57, no. 9 (September 2014): 82–89. http://dx.doi.org/10.1145/2629535.

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50

PRENDERGAST, CHRISTOPHER. "Rationality, Optimality, and Choice." Rationality and Society 5, no. 1 (January 1993): 47–57. http://dx.doi.org/10.1177/1043463193005001005.

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