D-Optimal Data Fusion: Exact and Approximation Algorithms

Published Online:https://doi.org/10.1287/ijoc.2022.0235

References

  • Achterberg T, Bixby RE, Gu Z, Rothberg E, Weninger D (2020) Presolve reductions in mixed integer programming. INFORMS J. Comput. 32(2):473–506.LinkGoogle Scholar
  • Ahmed S, Atamtürk A (2011) Maximizing a class of submodular utility functions. Math. Programming 128(1):149–169.CrossrefGoogle Scholar
  • Aminifar F, Khodaei A, Fotuhi-Firuzabad M, Shahidehpour M (2009) Contingency-constrained PMU placement in power networks. IEEE Trans. Power Systems 25(1):516–523.CrossrefGoogle Scholar
  • Anstreicher KM (2018) Maximum-entropy sampling and the Boolean quadric polytope. J. Global Optim. 72(4):603–618.CrossrefGoogle Scholar
  • Anstreicher KM (2020) Efficient solution of maximum-entropy sampling problems. Oper. Res. 68(6):1826–1835.LinkGoogle Scholar
  • Anstreicher KM, Lee J (2004) A masked spectral bound for maximum-entropy sampling. mODa 7—Advances in Model-Oriented Design and Analysis, Contributions in Statistics (Physica, Heidelberg), 1–12.CrossrefGoogle Scholar
  • Anstreicher KM, Fampa M, Lee J, Williams J (1996) Continuous relaxations for constrained maximum-entropy sampling. Integer Programming and Combinatorial Optimization (Vancouver, BC, 1996), Lecture Notes in Comput. Sci., vol. 1084 (Springer, Berlin), 234–248.CrossrefGoogle Scholar
  • Anstreicher KM, Fampa M, Lee J, Williams J (1999) Using continuous nonlinear relaxations to solve constrained maximum-entropy sampling problems. Math. Programming Series A 85(2):221–240.CrossrefGoogle Scholar
  • Burer S, Lee J (2007) Solving maximum-entropy sampling problems using factored masks. Math. Programming 109(2-3, Ser. B):263–281.CrossrefGoogle Scholar
  • Chen Z, Fampa M, Lee J (2023) On computing with some convex relaxations for the maximum-entropy sampling problem. INFORMS J. Comput. 35(2):368–385.LinkGoogle Scholar
  • Chen Z, Fampa M, Lambert A, Lee J (2021) Mixing convex-optimization bounds for maximum-entropy sampling. Math. Programming Series B 188:539–568.CrossrefGoogle Scholar
  • Choi H-L, How J, Barton P (2008) An outer-approximation algorithm for generalized maximum entropy sampling. Proc. of ACC 2008 (IEEE, Piscataway, NJ), 1818–1823.Google Scholar
  • Choi H-L, How J, Barton P (2013) An outer-approximation approach for information-maximizing sensor selection. Optim. Lett. 7:745–764.CrossrefGoogle Scholar
  • Civril A, Magdon-Ismail M (2013) Exponential inapproximability of selecting a maximum volume sub-matrix. Algorithmica 65(1):159–176.CrossrefGoogle Scholar
  • De La Ree J, Centeno V, Thorp JS, Phadke AG (2010) Synchronized phasor measurement applications in power systems. IEEE Trans. Smart Grid. 1(1):20–27.CrossrefGoogle Scholar
  • Fampa M, Lee J (2022a) Maximum-Entropy Sampling: Algorithms and Application (Springer, New York).CrossrefGoogle Scholar
  • Fampa M, Lee J (2022b) An outer-approximation algorithm for maximum-entropy sampling. Proc. of ISCO 2022, Lecture Notes in Computer Science (Springer).Google Scholar
  • Fischetti M, Lodi A (2010) Heuristics in Mixed Integer Programming (John Wiley & Sons, Inc., Hoboken, NJ).Google Scholar
  • He X (2009) Laplacian regularized d-optimal design for active learning and its application to image retrieval. IEEE Trans. Image Process. 19(1):254–263.Google Scholar
  • Hoffman A, Lee J, Williams J (2001) New upper bounds for maximum-entropy sampling. mODa 6—Advances in Model-Oriented Design and Analysis (Puchberg/Schneeberg, 2001), Contrib. Statist. (Physica, Heidelberg), 143–153.Google Scholar
  • Johnson CR, Horn RA (1985) Matrix Analysis (Cambridge University Press, Cambridge, UK).Google Scholar
  • Kekatos V, Giannakis GB (2011) A convex relaxation approach to optimal placement of phasor measurement units. Proc. 2011 4th IEEE Internat. Workshop Comput. Adv. Multi-Sensor Adaptive Processing (CAMSAP) (IEEE, Piscataway, NJ), 145–148.Google Scholar
  • Ko C-W, Lee J, Queyranne M (1995) An exact algorithm for maximum entropy sampling. Oper. Res. 43(4):684–691.LinkGoogle Scholar
  • Lapsins J, Cakula S (2021) Active machine learning in regression problems. Proc. 2021 IEEE Internat. Conf. Industrial Engrg. Engrg. Management (IEEM) (IEEE, Piscataway, NJ), 1020–1023.Google Scholar
  • Lee J (1998) Constrained maximum-entropy sampling. Oper. Res. 46(5):655–664.LinkGoogle Scholar
  • Lee J, Williams J (2003) A linear integer programming bound for maximum-entropy sampling. Math. Programming, Series B 94(2–3):247–256.CrossrefGoogle Scholar
  • Li Q, Negi R, Ilić MD (2011) Phasor measurement units placement for power system state estimation: A greedy approach. 2011 IEEE Power Energy Soc. General Meeting (IEEE, Piscataway, NJ), 1–8.Google Scholar
  • Li Q, Cui T, Weng Y, Negi R, Franchetti F, Ilic MD (2012) An information-theoretic approach to PMU placement in electric power systems. IEEE Trans. Smart Grid. 4(1):446–456.CrossrefGoogle Scholar
  • Li Y, Xie W (2020) Exact and approximation algorithms for sparse PCA. Preprint, submitted August 28, https://arXiv:2008.12438.Google Scholar
  • Li Y, Xie W (2023) Best principal submatrix selection for the maximum entropy sampling problem: Scalable algorithms and performance guarantees. Oper. Res., ePub ahead of print May 30, https://doi.org/10.1287/opre.2023.2488.LinkGoogle Scholar
  • Madan V, Singh M, Tantipongpipat U, Xie W (2019) Combinatorial algorithms for optimal design. Proc. Conf. Learn. Theory (PMLR, New York), 2210–2258.Google Scholar
  • Martinez S, Alaeddini A, Langer K (2019) A sequential weighted Laplacian-regularized optimal design for response surface modeling of expensive functions with outliers: An application in linear elastic fracture mechanics. Qual. Reliab. Engrg. Internat. 35(6):1911–1928.CrossrefGoogle Scholar
  • Melo W, Fampa M, Raupp F (2020) An overview of MINLP algorithms and their implementation in Muriqui Optimizer. Ann. Oper. Res. 286:217–241.CrossrefGoogle Scholar
  • Monticelli A (2000) Electric power system state estimation. Proc. IEEE. 88(2):262–282.CrossrefGoogle Scholar
  • Nemhauser GL, Wolsey LA, Fisher ML (1978) An analysis of approximations for maximizing submodular set functions—I. Math. Programming 14(1):265–294.CrossrefGoogle Scholar
  • Nikolov A (2015) Randomized rounding for the largest simplex problem. Proc. Forty-Seventh Ann. ACM Sympos. Theory Comput. (Association for Computing Machinery, New York), 861–870.Google Scholar
  • Nikolov A, Singh M (2016) Maximizing determinants under partition constraints. Proc. Forty-Eighth Ann. ACM Sympos. Theory Comput. (Association for Computing Machinery, New York), 192–201.Google Scholar
  • Nikolov A, Singh M, Tantipongpipat UT (2022) Proportional volume sampling and approximation algorithms for A-optimal design. Mathematics Oper. Res. 47(2):847–877.Google Scholar
  • Nuqui RF (2001) State estimation and voltage security monitoring using synchronized phasor measurements. PhD thesis, Virginia Polytechnic Institute and State University. https://vtechworks.lib.vt.edu/bitstream/handle/10919/28266/rnuqui_dissertation.pdf.Google Scholar
  • Palmgren J (1981) The fisher information matrix for log linear models arguing conditionally on observed explanatory variable. Biometrika 68(2):563–566.Google Scholar
  • Ponte G, Fampa M, Lee J (2022) Exact and heuristic solution approaches for the D-optimality problem. Proc. 54th Brazilian Sympos. Oper. Res.Google Scholar
  • Quesada I, Grossmann IE (1992) An LP/NLP based branch and bound algorithm for convex MINLP optimization problems. Comput. Chem. Engrg. 16(10–11):937–947.CrossrefGoogle Scholar
  • Shewry MC, Wynn HP (1987) Maximum entropy sampling. J. Appl. Statist. 14(2):165–170.CrossrefGoogle Scholar
  • Singh M, Xie W (2020) Approximation algorithms for D-optimal design. Math. Oper. Res. 45(4):1512–1534.LinkGoogle Scholar
  • Tantipongpipat U (2020) λ-regularized A-optimal design and its approximation by λ-regularized proportional volume sampling. Preprint, submitted June 19, https://arXiv:2006.11182.Google Scholar
  • Terzija V, Valverde G, Cai D, Regulski P, Madani V, Fitch J, Skok S, Begovic MM, Phadke A (2010) Wide-area monitoring, protection, and control of future electric power networks. Proc. IEEE. 99(1):80–93.CrossrefGoogle Scholar
  • Theodorakatos NP (2017) Application of synchronized phasor measurements units in power systems. Internat. J. Engrg. Sci. 6(3):25–39.CrossrefGoogle Scholar
  • Varshney PK (2012) Distributed Detection and Data Fusion (Springer, New York).Google Scholar
  • Wang J, Park E (2017) Active learning for penalized logistic regression via sequential experimental design. Neurocomputing 222:183–190.CrossrefGoogle Scholar
  • Wolsey LA, Nemhauser GL (1999) Integer and Combinatorial Optimization (John Wiley & Sons, New York).Google Scholar
  • Yang C, Kaplan L, Blasch E, Bakich M (2013) Optimal placement of heterogeneous sensors for targets with Gaussian priors. IEEE Trans. Aerosp. Electron. Syst. 49(3):1637–1653.CrossrefGoogle Scholar
  • Yu K, Bi J, Tresp V (2006) Active learning via transductive experimental design. Proc. 23rd Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 1081–1088.Google Scholar
  • Zhang H (1995) Two-dimensional optimal sensor placement. IEEE Trans. Syst. Man Cybern. 25(5):781–792.CrossrefGoogle Scholar
  • Zhao J, Tan J, Wu L, Zhan L, Yao W, Liu Y (2019) Impact of the measurement errors on synchrophasor-based WAMS applications. IEEE Access. 7:143960–143972.CrossrefGoogle Scholar
  • Zhou M, Centeno VA, Thorp JS, Phadke AG (2006) An alternative for including phasor measurements in state estimators. IEEE Trans. Power Syst. 21(4):1930–1937.CrossrefGoogle Scholar
  • Zimmerman RD, Murillo-Sánchez CE, Gan D (1997) Matpower: A MATLAB power system simulation package. Manual (Power Systems Engineering Research Center, Ithaca, NY).Google Scholar
  • Zimmerman RD, Murillo-Sánchez CE, Thomas RJ (2010) Matpower: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans. Power Syst. 26(1):12–19.CrossrefGoogle Scholar
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