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Journal articles on the topic 'Objective functions'

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Published: 4 June 2021
Last updated: 31 January 2023

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1

Neralić, Luka, and Sanjo Zlobec. "LFS functions in multi-objective programming." Applications of Mathematics 41, no. 5 (1996): 347–66. http://dx.doi.org/10.21136/am.1996.134331.

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2

Lauwers, Luc. "Intertemporal objective functions." Mathematical Social Sciences 35, no. 1 (January 1998): 37–55. http://dx.doi.org/10.1016/s0165-4896(97)00022-x.

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3

Rimer, Michael, and Tony Martinez. "Classification-based objective functions." Machine Learning 63, no. 2 (March 3, 2006): 183–205. http://dx.doi.org/10.1007/s10994-006-6266-6.

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4

Le Menestrel, Marc, and Luk N. Van Wassenhove. "Subjectively biased objective functions." EURO Journal on Decision Processes 4, no. 1-2 (January 9, 2015): 73–83. http://dx.doi.org/10.1007/s40070-014-0038-5.

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5

Bulger, David W., and H. Edwin Romeijn. "Optimising Noisy Objective Functions." Journal of Global Optimization 31, no. 4 (April 2005): 599–600. http://dx.doi.org/10.1007/s10898-004-9969-x.

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6

Lankoski, Leena, and Craig Smith. "Alternative Objective Functions for Firms." Academy of Management Proceedings 2016, no. 1 (January 2016): 13569. http://dx.doi.org/10.5465/ambpp.2016.13569abstract.

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7

Paulus, David M., and Richard A. Gaggioli. "Rational Objective Functions for Vehicles." Journal of Aircraft 40, no. 1 (January 2003): 27–34. http://dx.doi.org/10.2514/2.3090.

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8

Mjolsness, Eric, and Charles Garrett. "Algebraic transformations of objective functions." Neural Networks 3, no. 6 (January 1990): 651–69. http://dx.doi.org/10.1016/0893-6080(90)90055-p.

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9

Lankoski, Leena, and N. Craig Smith. "Alternative Objective Functions for Firms." Organization & Environment 31, no. 3 (September 1, 2017): 242–62. http://dx.doi.org/10.1177/1086026617722883.

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The predominant view of the role of business in society is that the objective of business is to maximize profit. Some argue that it ought to be something different. Others argue that for many firms it already is something different. However, the “something different” has not been fully fleshed out in its various versions. To address this gap, we define different relationship types between variables in an objective function and develop and present the resulting range of 10 alternative objective functions for firms. We then discuss how their development contributes to conceptual, empirical, and normative debates about organizational purpose. Removing the conventional assumption of profit maximization as the sole management principle opens up the possibility of new, more nuanced theoretical approaches to management. This article lays the groundwork for such theory development through the systematic and analytical identification of alternative objective functions that represent different specifications of firm purpose.
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10

Affif Chaouche, Fatima, Carrie Rutherford, and Robin Whitty. "Objective functions with redundant domains." Journal of Combinatorial Optimization 26, no. 2 (March 17, 2012): 372–84. http://dx.doi.org/10.1007/s10878-012-9468-9.

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11

Weber, Marcel. "How objective are biological functions?" Synthese 194, no. 12 (July 4, 2017): 4741–55. http://dx.doi.org/10.1007/s11229-017-1483-z.

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12

Lange, Kenneth, David R. Hunter, and Ilsoon Yang. "Optimization Transfer Using Surrogate Objective Functions." Journal of Computational and Graphical Statistics 9, no. 1 (March 2000): 1. http://dx.doi.org/10.2307/1390605.

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13

Lange, Kenneth, David R. Hunter, and Ilsoon Yang. "Optimization Transfer Using Surrogate Objective Functions." Journal of Computational and Graphical Statistics 9, no. 1 (March 2000): 1–20. http://dx.doi.org/10.1080/10618600.2000.10474858.

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14

Kanarachos, Andreas E., and Kleanthis T. Geramanis. "Fuzzy-Type Objective Functions for NNDC." IFAC Proceedings Volumes 31, no. 12 (June 1998): 127–32. http://dx.doi.org/10.1016/s1474-6670(17)36052-4.

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15

Nakamura, Yutaka. "Objective Belief Functions as Induced Measures." Theory and Decision 55, no. 1 (August 2003): 71–83. http://dx.doi.org/10.1023/b:theo.0000019053.53742.37.

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16

Frazer, L. Neil, and Xinhua Sun. "New objective functions for waveform inversion." GEOPHYSICS 63, no. 1 (January 1998): 213–22. http://dx.doi.org/10.1190/1.1444315.

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Inversion is an organized search for parameter values that maximize or minimize an objective function, referred to here as a processor. This note derives three new seismic processors that require neither prior deconvolution nor knowledge of the source‐receiver wavelet. The most powerful of these is the fourwise processor, as it is applicable to data sets from multiple shots and receivers even when each shot has a different unknown signature and each receiver has a different unknown impulse response. Somewhat less powerful than the fourwise processor is the pairwise processor, which is applicable to a data set consisting of two or more traces with the same unknown wavelet but possibly different gains. When only one seismogram exists the partition processor can be used. The partition processor is also applicable when there is only one shot (receiver) and each receiver (shot) has a different signature. In fourwise and pairwise inversions the unknown wavelets may be arbitrarily long in time and need not be minimum phase. In partition inversion the wavelet is assumed to be shorter in time than the data trace itself but is not otherwise restricted. None of the methods requires assumptions about the Green’s function.
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17

Brylawski, Thomas. "Greedy Families for Linear Objective Functions." Studies in Applied Mathematics 84, no. 3 (April 1991): 221–29. http://dx.doi.org/10.1002/sapm1991843221.

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18

Prekopa, Andreas. "Stochastic programming with multiple objective functions." European Journal of Operational Research 27, no. 2 (October 1986): 260. http://dx.doi.org/10.1016/0377-2217(86)90078-0.

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19

Otake, Shun, Tomohiro Yoshikawa, and Takeshi Furuhashi. "Basic Study on Assembling of Objective Functions in Many-Objective Optimization Problems." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 6 (September 20, 2010): 618–23. http://dx.doi.org/10.20965/jaciii.2010.p0618.

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Genetic Algorithms (GAs) have been widely applied to Multiobjective Optimization Problems (MOPs), called MOGA. A set of Pareto solutions in MOPs having plural fitness functions are searched, then GA is applied in a multipoint search. MOGA performance decreases with the increasing number of objective functions because solution space spreads exponentially. An effective MOGA search is an important issue in many objective optimization problems. One effective approach is assembling objective functions and reducing their number, but appropriate assembly and the number of objective functions to be assembled has not been studied sufficiently. Our purpose here is to determine the effects of assembling objective functions by studying assembly effects when MOGA is applied to a simplified Nurse Scheduling Problem (sNSP) in two types of assembly based on objective function meaning and correlation coefficients.
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20

CHARLES, V., and D. DUTTA. "IDENTIFICATION OF REDUNDANT OBJECTIVE FUNCTIONS IN MULTI-OBJECTIVE STOCHASTIC FRACTIONAL PROGRAMMING PROBLEMS." Asia-Pacific Journal of Operational Research 23, no. 02 (June 2006): 155–70. http://dx.doi.org/10.1142/s0217595906000863.

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Redundancy in constraints and variables are usually studied in linear, integer and non-linear programming problems. However, main emphasis has so far been given only to linear programming problems. In this paper, an algorithm that identifies redundant objective functions in multi-objective stochastic fractional programming problems is provided. A solution procedure is also illustrated. This reduces the number of objective functions in cases where redundant objective functions exist.
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21

Smirnov, A. V. "Properties of objective functions and search algorithms in multi-objective optimization problems." Russian Technological Journal 10, no. 4 (July 30, 2022): 75–85. http://dx.doi.org/10.32362/2500-316x-2022-10-4-75-85.

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Objectives. A frequently used method for obtaining Pareto-optimal solutions is to minimize a selected quality index under restrictions of the other quality indices, whose values are thus preset. For a scalar objective function, the global minimum is sought that contains the restricted indices as penalty terms. However, the landscape of such a function has steep-ascent areas, which significantly complicate the search for the global minimum. This work compared the results of various heuristic algorithms in solving problems of this type. In addition, the possibility of solving such problems using the sequential quadratic programming (SQP) method, in which the restrictions are not imposed as the penalty terms, but included into the Lagrange function, was investigated.Methods. The experiments were conducted using two analytically defined objective functions and two objective functions that are encountered in problems of multi-objective optimization of characteristics of analog filters. The corresponding algorithms were realized in the MATLAB environment.Results. The only heuristic algorithm shown to obtain the optimal solutions for all the functions is the particle swarm optimization algorithm. The sequential quadratic programming (SQP) algorithm was applicable to one of the analytically defined objective functions and one of the filter optimization objective functions, as well as appearing to be significantly superior to heuristic algorithms in speed and accuracy of solutions search. However, for the other two functions, this method was found to be incapable of finding correct solutions.Conclusions. A topical problem is the estimation of the applicability of the considered methods to obtaining Pareto-optimal solutions based on preliminary analysis of properties of functions that determine the quality indices.
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22

Arruda, Jose Roberto F. "Objective functions for the nonlinear curve fit of frequency response functions." AIAA Journal 30, no. 3 (March 1992): 855–57. http://dx.doi.org/10.2514/3.11001.

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23

Terazono, Yasushi, and Ayumu Matani. "Continuity of Optimal Solution Functions and their Conditions on Objective Functions." SIAM Journal on Optimization 25, no. 4 (January 2015): 2050–60. http://dx.doi.org/10.1137/110850189.

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24

Hill, T. P., and D. P. Kennedy. "Optimal stopping problems with generalized objective functions." Journal of Applied Probability 27, no. 4 (December 1990): 828–38. http://dx.doi.org/10.2307/3214826.

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Optimal stopping of a sequence of random variables is studied, with emphasis on generalized objectives which may be non-monotone functions of EXt, where t is a stopping time, or may even depend on the entire vector (E[X1I{t=l}], · ··, E[XnI{t=n}]), such as the minimax objective to maximize minj{E[XjI{t=j}]}. Convexity is used to establish a prophet inequality and universal bounds for the optimal return, and a method for constructing optimal stopping times for such objectives is given.
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25

Kiers, Henk A. L. "[Optimization Transfer Using Surrogate Objective Functions]: Discussion." Journal of Computational and Graphical Statistics 9, no. 1 (March 2000): 21. http://dx.doi.org/10.2307/1390606.

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26

de Leeuw, Jan, and George Michailidis. "[Optimization Transfer Using Surrogate Objective Functions]: Discussion." Journal of Computational and Graphical Statistics 9, no. 1 (March 2000): 26. http://dx.doi.org/10.2307/1390607.

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27

Wu, Ying Nian. "[Optimization Transfer Using Surrogate Objective Functions]: Discussion." Journal of Computational and Graphical Statistics 9, no. 1 (March 2000): 32. http://dx.doi.org/10.2307/1390608.

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28

Meng, Xiao-Li. "[Optimization Transfer Using Surrogate Objective Functions]: Discussion." Journal of Computational and Graphical Statistics 9, no. 1 (March 2000): 35. http://dx.doi.org/10.2307/1390609.

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29

Groenen, Patrick J. F., and Willem J. Heiser. "[Optimization Transfer Using Surrogate Objective Functions]: Discussion." Journal of Computational and Graphical Statistics 9, no. 1 (March 2000): 44. http://dx.doi.org/10.2307/1390610.

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30

Gelman, Andrew. "[Optimization Transfer Using Surrogate Objective Functions]: Discussion." Journal of Computational and Graphical Statistics 9, no. 1 (March 2000): 49. http://dx.doi.org/10.2307/1390611.

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31

Hunter, David R., and Kenneth Lange. "[Optimization Transfer Using Surrogate Objective Functions]: Rejoinder." Journal of Computational and Graphical Statistics 9, no. 1 (March 2000): 52. http://dx.doi.org/10.2307/1390612.

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32

Steinberg, Richard. "The Revealed Objective Functions of Nonprofit Firms." RAND Journal of Economics 17, no. 4 (1986): 508. http://dx.doi.org/10.2307/2555478.

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33

Booth, James G., George Casella, and James P. Hobert. "Clustering using objective functions and stochastic search." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70, no. 1 (January 4, 2008): 119–39. http://dx.doi.org/10.1111/j.1467-9868.2007.00629.x.

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34

ADACHI, Eiji, and Hideki MATSUOKA. "Multiobjective Design Satisfaction with Heterogeneous Objective Functions." JSME international journal. Ser. 3, Vibration, control engineering, engineering for industry 35, no. 3 (1992): 500–504. http://dx.doi.org/10.1299/jsmec1988.35.500.

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35

MELLER, R. D., and K. Y. GAU. "Facility layout objective functions and robust layouts." International Journal of Production Research 34, no. 10 (October 1996): 2727–42. http://dx.doi.org/10.1080/00207549608905055.

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36

Makino, Yoshikazu, and Ilan Kroo. "Robust Objective Functions for Sonic-Boom Minimization." Journal of Aircraft 43, no. 5 (September 2006): 1301–6. http://dx.doi.org/10.2514/1.19442.

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37

Mendonça, L. F., J. M. Sousa, and J. M. Sá da Costa. "FUZZY OBJECTIVE FUNCTIONS IN MULTIVARIABLE PREDICTIVE CONTROL." IFAC Proceedings Volumes 35, no. 1 (2002): 103–8. http://dx.doi.org/10.3182/20020721-6-es-1901.00670.

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38

ADACHI, eiji, and Hideki MATSUOKA. "Multiobjective Design Satisfaction with Heterogeneous Objective Functions." Transactions of the Japan Society of Mechanical Engineers Series C 57, no. 539 (1991): 2483–86. http://dx.doi.org/10.1299/kikaic.57.2483.

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39

Xu, Di, and Susan L. Albin. "Robust Optimization of Experimentally Derived Objective Functions." IIE Transactions 35, no. 9 (September 2003): 793–802. http://dx.doi.org/10.1080/07408170304408.

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40

Karidis, J. P., and S. R. Turns. "Efficient Optimization of Computationally Expensive Objective Functions." Journal of Mechanisms, Transmissions, and Automation in Design 108, no. 3 (September 1, 1986): 336–39. http://dx.doi.org/10.1115/1.3258736.

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An algorithm is presented for the efficient constrained or unconstrained minimization of computationally expensive objective functions. The method proceeds by creating and numerically optimizing a sequence of surrogate functions which are chosen to approximate the behavior of the unknown objective function in parameter-space. The Recursive Surrogate Optimization (RSO) technique is intended for design applications where the computational cost required to evaluate the objective function greatly exceeds both the cost of evaluating any domain constraints present and the cost associated with one iteration of a typical optimization routine. Efficient optimization is achieved by reducing the number of times that the objective function must be evaluated at the expense of additional complexity and computational cost associated with the optimization procedure itself. Comparisons of the RSO performance on eight widely used test problems to published performance data for other efficient techniques demonstrate the utility of the method.
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41

Zhang, Yan. "Experimental comparison of superquadric fitting objective functions." Pattern Recognition Letters 24, no. 14 (October 2003): 2185–93. http://dx.doi.org/10.1016/s0167-8655(02)00400-2.

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42

Hill, T. P., and D. P. Kennedy. "Optimal stopping problems with generalized objective functions." Journal of Applied Probability 27, no. 04 (December 1990): 828–38. http://dx.doi.org/10.1017/s002190020002800x.

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Abstract:
Optimal stopping of a sequence of random variables is studied, with emphasis on generalized objectives which may be non-monotone functions ofEXt, wheretis a stopping time, or may even depend on the entire vector (E[X1I{t=l}], · ··,E[XnI{t=n}]),such as the minimax objective to maximize minj{E[XjI{t=j}]}.Convexity is used to establish a prophet inequality and universal bounds for the optimal return, and a method for constructing optimal stopping times for such objectives is given.
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43

Zhang, Yan, and JingTao Yao. "Gini objective functions for three-way classifications." International Journal of Approximate Reasoning 81 (February 2017): 103–14. http://dx.doi.org/10.1016/j.ijar.2016.11.005.

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44

Kaminski, B. "On quasi-orderings and multi-objective functions." European Journal of Operational Research 177, no. 3 (March 2007): 1591–98. http://dx.doi.org/10.1016/j.ejor.2005.10.015.

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45

Verma, Rakesh Kumar. "Fuzzy geometric programming with several objective functions." Fuzzy Sets and Systems 35, no. 1 (March 1990): 115–20. http://dx.doi.org/10.1016/0165-0114(90)90024-z.

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46

Knorr, A. L., R. Jain, and R. Srivastava. "Bayesian-based selection of metabolic objective functions." Bioinformatics 23, no. 3 (December 6, 2006): 351–57. http://dx.doi.org/10.1093/bioinformatics/btl619.

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47

Harris, David G. "Deterministic Parallel Algorithms for Bilinear Objective Functions." Algorithmica 81, no. 3 (June 22, 2018): 1288–318. http://dx.doi.org/10.1007/s00453-018-0471-0.

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48

Zhang, C. N., J. H. Weston, and Y. F. Yan. "Determining objective functions in systolic array designs." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2, no. 3 (September 1994): 357–60. http://dx.doi.org/10.1109/92.311644.

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49

LEVARY, REUVEN R. "Optimal control problems with goal objective functions." International Journal of Systems Science 17, no. 1 (January 1986): 97–109. http://dx.doi.org/10.1080/00207728608926786.

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50

Weir, T., and B. Mond. "Pre-invex functions in multiple objective optimization." Journal of Mathematical Analysis and Applications 136, no. 1 (November 1988): 29–38. http://dx.doi.org/10.1016/0022-247x(88)90113-8.

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