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Journal articles on the topic 'Optimization measures'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Gambrah, Priscilla, and Traian Pirvu. "Risk Measures and Portfolio Optimization." Journal of Risk and Financial Management 7, no. 3 (2014): 113–29. http://dx.doi.org/10.3390/jrfm7030113.

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2

MIRANDA, PEDRO, and MICHEL GRABISCH. "OPTIMIZATION ISSUES FOR FUZZY MEASURES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 06 (1999): 545–60. http://dx.doi.org/10.1142/s0218488599000477.

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In this paper, we address the problem of identification of fuzzy measures through different representations, namely the Möbius, the Shapley and the Banzhaf interaction representations. In the first part of the paper, we recall the main results concerning these representations, and give a simple algorithm to compute them. Then we determine the bounds of the Möbius and the interaction representations for fuzzy measures. Lastly, the identification of fuzzy measures by minimizing a quadratic error criterion is addressed. We give expressions of the quadratic program for all the considered represent

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3

Schal, Manfred. "Portfolio Optimization and Martingale Measures." Mathematical Finance 10, no. 2 (2000): 289–303. http://dx.doi.org/10.1111/1467-9965.00095.

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4

Chudzian, Paweł. "Evaluation measures for kernel optimization." Pattern Recognition Letters 33, no. 9 (2012): 1108–16. http://dx.doi.org/10.1016/j.patrec.2012.01.006.

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5

Pedregal, Pablo. "Optimization, relaxation and Young measures." Bulletin of the American Mathematical Society 36, no. 01 (1999): 27–59. http://dx.doi.org/10.1090/s0273-0979-99-00774-0.

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6

Tomić, Bojan, Saša Žiković, and Lorena Jovanović. "CRYPTO PORTFOLIO OPTIMIZATION THROUGH LENS OF TAIL RISK AND VARIANCE MEASURES." Zbornik radova Ekonomskog fakulteta u Rijeci: časopis za ekonomsku teoriju i praksu/Proceedings of Rijeka Faculty of Economics: Journal of Economics and Business 40, no. 2 (2022): 297–312. http://dx.doi.org/10.18045/zbefri.2022.2.297.

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The choice of an adequate risk measure in portfolio optimization depends to a large extent on the characteristics and dynamics of the underlying assets. For investors and asset managers, a range of potential market risks provides much- needed insights into the optimization of their portfolio of assets. Since this paper focuses on multiple risk measures, it presents the investors with a better insight into the potential magnitude of the risk they are faced with. Since the risk-reward optimization target can be adjusted for a broad choice of risk measures in this paper we will test the performan

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7

Bright, Ido, Qinfeng Li, and Monica Torres. "Occupational measures and averaged shape optimization." ESAIM: Control, Optimisation and Calculus of Variations 24, no. 3 (2018): 1141–65. http://dx.doi.org/10.1051/cocv/2017017.

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We consider the minimization of averaged shape optimization problems over the class of sets of finite perimeter. We use occupational measures, which are probability measures defined in terms of the reduced boundary of sets of finite perimeter, that allow to transform the minimization into a linear problem on a set of measures. The averaged nature of the problem allows the optimal value to be approximated with sets with unbounded perimeter. In this case, we show that we can also approximate the optimal value with convex polytopes with n+1 faces shrinking to a point. We derive conditions under w

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8

Schied*, Alexander. "Risk Measures and Robust Optimization Problems." Stochastic Models 22, no. 4 (2006): 753–831. http://dx.doi.org/10.1080/15326340600878677.

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9

Mastrogiacomo, Elisa, and Emanuela Rosazza Gianin. "Portfolio Optimization with Quasiconvex Risk Measures." Mathematics of Operations Research 40, no. 4 (2015): 1042–59. http://dx.doi.org/10.1287/moor.2015.0711.

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10

Jenkinson, Oliver. "Optimization and majorization of invariant measures." Electronic Research Announcements of the American Mathematical Society 13, no. 1 (2007): 1–12. http://dx.doi.org/10.1090/s1079-6762-07-00170-9.

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11

Zhang, Xili, Wei-Guo Zhang, and Ruichu Cai. "Portfolio adjusting optimization under credibility measures." Journal of Computational and Applied Mathematics 234, no. 5 (2010): 1458–65. http://dx.doi.org/10.1016/j.cam.2010.02.022.

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12

Lagos, Guido, Daniel Espinoza, Eduardo Moreno, and Juan Pablo Vielma. "Restricted risk measures and robust optimization." European Journal of Operational Research 241, no. 3 (2015): 771–82. http://dx.doi.org/10.1016/j.ejor.2014.09.024.

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13

Čertický, Martin, and Peter Sinčák. "User Experience Optimization Using Psychophisiological Measures." Acta Electrotechnica et Informatica 16, no. 3 (2016): 48–53. http://dx.doi.org/10.15546/aeei-2016-0023.

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14

Kordestani, Javidan Kazemi, Alireza Rezvanian, and Mohammad Reza Meybodi. "New measures for comparing optimization algorithms on dynamic optimization problems." Natural Computing 18, no. 4 (2017): 705–20. http://dx.doi.org/10.1007/s11047-016-9596-8.

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15

Embrechts, Paul, Alexander Schied, and Ruodu Wang. "Robustness in the Optimization of Risk Measures." Operations Research 70, no. 1 (2022): 95–110. http://dx.doi.org/10.1287/opre.2021.2147.

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We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk-measurement-related optimization problem is robust, which we call “robustness against optimization.” The new notion is studied for various classes of risk measures and expected utility and loss functions. Motivated by practical issues from !nancial regulation, special attention is given to the twomost widely used riskmeasures in the industry, Valueat-Risk (VaR) and Expected Shortfall (ES).We establish that for a class of general op

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16

Trzpiot, Grażyna. "Application of coherent distortion risk measures." Statistics in Transition new series 15, no. 2 (2014): 283–98. http://dx.doi.org/10.59170/stattrans-2014-019.

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This paper concentrates on solving the portfolio selection problem. It starts with an extension of the well-known optimization framework for Conditional Value-at-Risk (CVaR)-based portfolio selection problems [1, 2] to optimization over a more general class of risk measure - known as the class of Coherent Distortion Risk Measure (CDRM). The CDRM class of risk measures is the intersection of Coherent Risk Measure (CRM) and Distortion Risk Measure (DRM). It concludes with showing that many of the well-known risk measures are of special cases of the CDRM class what may facilitate to deal with the

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17

Krbcova, Zuzana. "Stress Measures in SOM Learning." MENDEL 24, no. 1 (2018): 107–12. http://dx.doi.org/10.13164/mendel.2018.1.107.

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Various stress measures can be used in generalized version of Sammon’s mapping. Kohonen SOM with iterative or batch learning is a standard tool for data self-organization, too. Our method applies stress functions to pattern relationships in SOM and converts batch learning to discrete optimization task. Due to NP–completeness of SOM learning, optimization heuristics have to be used. Simulated annealing making use of Lévy flights is the recommended heuristics for this task.

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18

ARARAT, Çağın. "Portfolio optimization with two quasiconvex risk measures." TURKISH JOURNAL OF MATHEMATICS 45, no. 2 (2021): 695–717. http://dx.doi.org/10.3906/mat-2012-45.

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19

Bucur, Dorin, Giuseppe Buttazzo, and Bozhidar Velichkov. "Spectral Optimization Problems for Potentials and Measures." SIAM Journal on Mathematical Analysis 46, no. 4 (2014): 2956–86. http://dx.doi.org/10.1137/130939808.

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20

Naimimohasses, R., D. M. Barnett, D. A. Green, and P. R. Smith. "Sensor optimization using neural network sensitivity measures." Measurement Science and Technology 6, no. 9 (1995): 1291–300. http://dx.doi.org/10.1088/0957-0233/6/9/008.

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21

Singh, Shilpi, and Raj Shree. "Different Similarity Measures for Test Suite Optimization." Advanced Science, Engineering and Medicine 10, no. 7 (2018): 833–36. http://dx.doi.org/10.1166/asem.2018.2234.

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22

Velten, K., P. J. Paschold, and A. Stahel. "Optimization of cultivation measures affecting soil temperature." Scientia Horticulturae 97, no. 2 (2003): 163–84. http://dx.doi.org/10.1016/s0304-4238(02)00149-8.

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23

Aktürk, Tahsin Deniz, and Çağın Ararat. "Portfolio optimization with two coherent risk measures." Journal of Global Optimization 78, no. 3 (2020): 597–626. http://dx.doi.org/10.1007/s10898-020-00922-y.

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24

Vercher, Enriqueta, José D. Bermúdez, and José Vicente Segura. "Fuzzy portfolio optimization under downside risk measures." Fuzzy Sets and Systems 158, no. 7 (2007): 769–82. http://dx.doi.org/10.1016/j.fss.2006.10.026.

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25

Robertson, Stephen, and Hugo Zaragoza. "On rank-based effectiveness measures and optimization." Information Retrieval 10, no. 3 (2007): 321–39. http://dx.doi.org/10.1007/s10791-007-9025-9.

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26

Kirilyuk, V. S. "Polyhedral Coherent Risk Measures and Robust Optimization." Cybernetics and Systems Analysis 55, no. 6 (2019): 999–1008. http://dx.doi.org/10.1007/s10559-019-00210-y.

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27

Zhang, Xili, Weiguo Zhang, and Weilin Xiao. "Multi-period portfolio optimization under possibility measures." Economic Modelling 35 (September 2013): 401–8. http://dx.doi.org/10.1016/j.econmod.2013.07.023.

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28

Eichhorn, Andreas, Werner Römisch, and Isabel Wegner. "Polyhedral risk measures in electricity portfolio optimization." PAMM 4, no. 1 (2004): 7–10. http://dx.doi.org/10.1002/pamm.200410002.

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29

Mastrogiacomo, Elisa, and Matteo Rocca. "Set optimization of set-valued risk measures." Annals of Operations Research 296, no. 1-2 (2020): 291–314. http://dx.doi.org/10.1007/s10479-020-03541-8.

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30

Miller, Naomi, and Andrzej Ruszczyński. "Risk-adjusted probability measures in portfolio optimization with coherent measures of risk." European Journal of Operational Research 191, no. 1 (2008): 193–206. http://dx.doi.org/10.1016/j.ejor.2007.06.052.

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31

Geissel, S., H. Graf, J. Herbinger, and F. T. Seifried. "Portfolio optimization with optimal expected utility risk measures." Annals of Operations Research 309, no. 1 (2021): 59–77. http://dx.doi.org/10.1007/s10479-021-04403-7.

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AbstractThe purpose of this article is to evaluate optimal expected utility risk measures (OEU) in a risk-constrained portfolio optimization context where the expected portfolio return is maximized. We compare the portfolio optimization with OEU constraint to a portfolio selection model using value at risk as constraint. The former is a coherent risk measure for utility functions with constant relative risk aversion and allows individual specifications to the investor’s risk attitude and time preference. In a case study with three indices, we investigate how these theoretical differences influ

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32

Ghanbari, Hossein, Mojtaba Safari, Rouzbeh Ghousi, Emran Mohammadi, and Nawapon Nakharutai. "Bibliometric analysis of risk measures for portfolio optimization." Accounting 9, no. 2 (2023): 95–108. http://dx.doi.org/10.5267/j.ac.2022.12.003.

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Portfolio optimization aims to minimize risk and maximize return on investment by determining the best combination of securities and proportions. The variance in portfolio optimization models is typically used for a measure of risk. Over the last few decades, portfolio optimization utilizing a variety of risk measures has grown significantly, and many studies have been conducted. Therefore, this paper provides a systematic review of risk measures for portfolio optimization using bibliometric analysis and maps to analyze the evolution and trends of 682 articles published between 2000 and 2022.

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33

CHEKHLOV, ALEXEI, STANISLAV URYASEV, and MICHAEL ZABARANKIN. "DRAWDOWN MEASURE IN PORTFOLIO OPTIMIZATION." International Journal of Theoretical and Applied Finance 08, no. 01 (2005): 13–58. http://dx.doi.org/10.1142/s0219024905002767.

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A new one-parameter family of risk measures called Conditional Drawdown (CDD) has been proposed. These measures of risk are functionals of the portfolio drawdown (underwater) curve considered in active portfolio management. For some value of the tolerance parameter α, in the case of a single sample path, drawdown functional is defined as the mean of the worst (1 - α) * 100% drawdowns. The CDD measure generalizes the notion of the drawdown functional to a multi-scenario case and can be considered as a generalization of deviation measure to a dynamic case. The CDD measure includes the Maximal Dr

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34

Huang, Shu Ping, Jian Yun Fu, and Yan Cai Li. "Temperature Control Measures Optimization of RCC Gravity Dam." Applied Mechanics and Materials 226-228 (November 2012): 1153–56. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.1153.

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With the continuous development of dam construction technology, the RCC dam becomes one of the most popular types of dam in the world with its unique advantages. Temperature control measures research is one of the key issues of design and construction of mass concrete structures. How to choose the proper temperature control measures to prevent concrete cracks becomes the important problem of dam construction technology. In a RCC gravity dam, the climate environment is so severe that a single temperature control measure can’t meet the requirements of temperature control and crack prevention. In

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35

Bohvalovs, Girts, Ruta Vanaga, Vita Brakovska, Ritvars Freimanis, and Andra Blumberga. "Energy Community Measures Evaluation via Differential Evolution Optimization." Environmental and Climate Technologies 26, no. 1 (2022): 606–15. http://dx.doi.org/10.2478/rtuect-2022-0046.

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Abstract Energy communities are paving the way for new cooperation opportunities related to energy consumption and energy production. Individuals unite in energy communities to reduce the costs related to energy consumption. Although previous work has mainly focused on energy exchange inside the community. This work aims to investigate the Pareto-optimal solutions to the transformation of a historical district into an energy community. For energy efficiency and production measure calculation, a system dynamics model is developed. Multiobjective differential evolution optimization method is emp

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36

Pulsipher, Joshua L., Benjamin R. Davidson, and Victor M. Zavala. "New Measures for Shaping Trajectories in Dynamic Optimization." IFAC-PapersOnLine 55, no. 7 (2022): 495–500. http://dx.doi.org/10.1016/j.ifacol.2022.07.492.

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37

Li, Nan, Ivor W. Tsang, and Zhi-Hua Zhou. "Efficient Optimization of Performance Measures by Classifier Adaptation." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 6 (2013): 1370–82. http://dx.doi.org/10.1109/tpami.2012.172.

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38

KONDOR, IMRE, and ISTVÁN VARGA-HASZONITS. "INSTABILITY OF PORTFOLIO OPTIMIZATION UNDER COHERENT RISK MEASURES." Advances in Complex Systems 13, no. 03 (2010): 425–37. http://dx.doi.org/10.1142/s0219525910002591.

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It is shown that the axioms for coherent risk measures imply that whenever there is a pair of portfolios such that one of them dominates the other in a given sample (which happens with finite probability even for large samples), then there is no optimal portfolio under any coherent measure on that sample, and the risk measure diverges to minus infinity. This instability was first discovered in the special example of Expected Shortfall which is used here both as an illustration and as a springboard for generalization.

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39

Wang, P. K. C. "A class of optimization problems involving set measures." Nonlinear Analysis: Theory, Methods & Applications 47, no. 1 (2001): 25–36. http://dx.doi.org/10.1016/s0362-546x(01)00153-5.

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40

Curry, David M., and Cihan H. Dagli. "Computational Complexity Measures for Many-objective Optimization Problems." Procedia Computer Science 36 (2014): 185–91. http://dx.doi.org/10.1016/j.procs.2014.09.077.

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41

He, Shengwu. "Optimization Applications of Compensators of Poisson Random Measures." Probability in the Engineering and Informational Sciences 3, no. 1 (1989): 149–55. http://dx.doi.org/10.1017/s0269964800001030.

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Compensator or dual predictable projection is one of the main concepts of the general theory of stochastic processes, established by a French probabilistic school. In general, it is a considerably abstract concept and difficult to grasp. But in the Poisson case, it affords very powerful tools to calculate the expectation of certain stochastic integrals with respect to Poisson processes and random measures. Naturally, compensators can be used to solve certain problems of applied probability, involved in Poisson processes and random measures. In this direction, Brémaud and Jacod [1] had used the

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42

Cassioli, Andrea, Marco Locatelli, and Fabio Schoen. "Dissimilarity measures for population-based global optimization algorithms." Computational Optimization and Applications 45, no. 2 (2008): 257–81. http://dx.doi.org/10.1007/s10589-008-9194-5.

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43

Kirilyuk, V. S. "Polyhedral coherent risk measures and investment portfolio optimization." Cybernetics and Systems Analysis 44, no. 2 (2008): 250–60. http://dx.doi.org/10.1007/s10559-008-0025-6.

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44

Lin, Fan, Jingbin Wang, Nian Zhang, Jianbing Xiahou, and Nancy McDonald. "Multi-kernel learning for multivariate performance measures optimization." Neural Computing and Applications 28, no. 8 (2016): 2075–87. http://dx.doi.org/10.1007/s00521-015-2164-9.

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45

Holevo, A. S. "On Optimization Problem for Positive Operator-Valued Measures." Lobachevskii Journal of Mathematics 43, no. 7 (2022): 1646–50. http://dx.doi.org/10.1134/s1995080222100158.

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46

Huang, Chaomin. "Discussion on Optimization Measures of Building Construction Management Technology." Journal of Architectural Research and Development 7, no. 1 (2023): 17–24. http://dx.doi.org/10.26689/jard.v7i1.4695.

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Building construction needs have expanded in line with people's demands and the quality of life in today’s society. Therefore, the traditional construction management technology can no longer meet the current management and construction requirements, so it is necessary to further optimize the construction management technology. Therefore, this paper focuses on exploring measures regarding building construction technology optimization. Firstly, the paper briefly expounds its optimization value, then systematically analyzes some problems faced by the current housing construction management, and

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47

Richman Miyambu, Gezani, and Solly Matshonisa Seeletse. "Numeric measurement of business process optimization." Environmental Economics 7, no. 4 (2016): 20–24. http://dx.doi.org/10.21511/ee.07(4).2016.02.

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The paper describes a simple, straightforward method to measure progress of business process optimization (BPO). The aim is to derive measures of the degree of BPO attainment in order to identify future priority focus for ensuing exercises. These measures can help to identify components of business that should be improved towards full optimization of processes in business. In an ideal case of the business containing all the components, a large business scenario is assumed. However, flexibility is permissible when changes are experienced with either some business aspects missing or new ones add

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48

Kirilyuk, Vladimir. "On Polyhedral Coherent Risk Measures and Portfolio Optimization Problems." Cybernetics and Computer Technologies, no. 3 (November 29, 2022): 46–55. http://dx.doi.org/10.34229/2707-451x.22.3.5.

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Introduction. The problem of decision-making under risk and uncertainty lies in the use of adequate criteria for assessing their optimality, in particular, in an adequate risk assessment. Various functions are known that are used as risk measures. For technical systems, the probability of an accident (failure) is used, in insurance – the probability of bankruptcy, in finance – Value-at-Risk, etc. At present, the concept of a coherent risk measure (CRM), in which its basic properties are postulated, is widely recognized. The paper considers CRMs and their subset, the polyhedral CRMs (PCMRs), wh

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49

Wen, Zhiyuan, and Meirong Zhang. "On the optimization problems of the principal eigenvalues of measure differential equations with indefinite measures." Discrete & Continuous Dynamical Systems - B 25, no. 8 (2020): 3257–74. http://dx.doi.org/10.3934/dcdsb.2020061.

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50

Lima, Alessandra Marques. "Optimization of Compliance and Mitigation of Accounting Frauds in American Organizations." Núcleo do Conhecimento 06, no. 07 (2023): 118–33. https://doi.org/10.32749/nucleodoconhecimento.com.br/business-administration/optimization-of-compliance.

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It is through fraudulent accounting records that organizations' annual results invariably lead directors and stakeholders to substantial losses, if not bankruptcy. The literature shows that financial frauds result from improper accounting entries, stemming from the bad intentions and greed of corrupt individuals in top management positions in various businesses. These individuals choose to engage in conduct that harms entire groups composed of companies, workers, and investors, affecting their credibility and integrity in their industry and also impacting professionals in the fields of account

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