dc.date2016
dc.date2016-06-03T20:13:57Z
dc.date2016-06-03T20:13:57Z
dc.date.accessioned2018-03-29T01:32:57Z
dc.date.available2018-03-29T01:32:57Z
dc.identifier
dc.identifierOptimization Methods And Software. Taylor And Francis Ltd., v. 31, n. 2, p. 328 - 341, 2016.
dc.identifier10556788
dc.identifier10.1080/10556788.2015.1082105
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84955679443&partnerID=40&md5=b42f285fedac8000c0df50edbf3c0f50
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/238145
dc.identifier2-s2.0-84955679443
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1304806
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionAn ensemble of quantum states can be described by a Hermitian, positive semidefinite and unit trace matrix called density matrix. Thus, the study of methods for optimizing a certain function (energy, entropy) over the set of density matrices has a direct application to important problems in quantum information and computation. We propose a projected gradient method for solving such problems. By exploiting the geometry of the feasible set, which is the intersection of the cone of Hermitian positive semidefinite matrices with the hyperplane defined by the unit trace constraint, we describe an efficient procedure to compute the projection onto this set using the Frobenius norm. Some important applications, such as quantum state tomography, are described and numerical experiments illustrate the effectiveness of the method when compared to previous methods based on fixed-point iterations or semidefinite programming. © 2015 Taylor & Francis.
dc.description31
dc.description2
dc.description328
dc.description341
dc.descriptionCNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionAnderson, E., Bai, Z., Bischof, C., Blackford, S., Demmel, J., Dongarra, J., Du Croz, J., Sorensen, D., (1999) LAPACK Users' Guide, , 3rd ed., SIAM, Philadelphia, PA
dc.descriptionBarzilai, J., Borwein, J.M., Two-point step size gradient methods (1988) IMA J. Numer. Anal, 8, pp. 141-148
dc.descriptionBertsekas, D., (1999) Nonlinear Programming, , 2nd ed., Athena Scientific
dc.descriptionBertsekas, D., Nedic, A., Ozdaglar, A.E., (2003) Convex Analysis and Optimization, , Athena Scientific, Belmont
dc.descriptionBirgin, E.G., Martínez, J.M., (2014) Augmented Lagrangian Methods for Constrained Optimization, , SIAM, Philadelphia, PA
dc.descriptionBirgin, E.G., Martínez, J.M., Raydan, M., Nonmonotone spectral projected gradient methods on convex sets (2000) SIAM J. Optimiz, 10, pp. 1196-1211
dc.descriptionBirgin, E.G., Martínez, J.M., Raydan, M., Inexact spectral projected gradient methods on convex sets (2003) IMA J. Numer. Anal, 23, pp. 539-559
dc.descriptionBoyd, S., Vandenberghe, L., (2004) Convex Optimization, , Cambridge University Press, New York
dc.descriptionBoyle, J.P., Dykstra, R.L., A method for finding projections onto the intersection of convex sets in Hilbert spaces (1986) Lecture Notes in Statistics, 37, pp. 28-47
dc.descriptionBrandão, F.G.S.L., Vianna, R.O., Robust semidefinite programming approach to the separability problem (2004) Phys. Rev. A, 70
dc.descriptionBuzek, V., Drobny, G., Derka, R., Adam, G., Wiedemann, H., Quantum state reconstruction from incomplete data (1999) Chaos Solitons Fractals, 10, pp. 981-1074
dc.descriptionCausa, A., Raciti, F., A purely geometric approach to the problem of computing the projection of a point on a simplex (2013) J. Optimiz. Theory Appl., 156, pp. 524-528
dc.descriptionCuppen, J.J.M., A divide and conquer method for the symmetric tridiagonal eigenproblem (1980) Numer. Math., 36, pp. 177-195
dc.descriptionDemmel, J., (1997) Applied Numerical Linear Algebra, , SIAM, Philadelphia
dc.descriptionDoherty, A.C., Parrilo, P.A., Spedalieri, F.M., Distinguishing separable and entangled states (2002) Phys. Rev. Lett., 88
dc.descriptionDuchi, J., Shalev-Shwartz, S., Singer, Y., Chandra, T., Efficient projections onto the l1 -ball for Learning in High Dimensions (2008) Proceedings of the 25th International Conference on Machine Learning, ICML '08, pp. 272-279. , Helsinki, Finland, ACM
dc.descriptionGlancy, S., Knill, E., Girard, M., Gradient-based stopping rules for maximum-likelihood quantum-state tomography (2012) New J. Phys., 14
dc.descriptionGolub, G., Van Loan, C., (1996) Matrix Computations, , 3rd ed., Johns Hopkins University Press, Baltimore
dc.descriptionGómez, W., Ramírez, H., A filter algorithm for nonlinear semidefinite programming (2010) Comput. Appl. Math., 29, pp. 297-328
dc.descriptionGonçalves, D.S., Gomes-Ruggiero, M.A., Lavor, C., Global convergence of diluted iterations in maximum-likelihood quantum tomography (2014) Quant. Inf. Comput, 14, pp. 966-980
dc.descriptionGonçalves, D.S., Lavor, C., Gomes-Ruggiero, M.A., Cesário, A.T., Vianna, R.O., Maciel, T.O., Quantum state tomography with incomplete data: Maximum entropy and variational quantum tomography (2013) Phys. Rev. A, 87
dc.descriptionGrippo, L., Lampariello, F., Lucidi, S., A nonmonotone line search technique for Newton's method (1986) SIAM J. Numer. Anal, 23, pp. 707-716
dc.descriptionGu, M., Eisenstat, S.C., A divide-and-conquer algorithm for the symmetric tridiagonal eigenproblem (1995) SIAM J. Matrix Anal. Appl., 16, pp. 172-191
dc.descriptionHorodecki, M., Horodecki, P., Horodecki, R., Separability of mixed states: Necessary and sufficient conditions (1996) Phys. Lett. A, 223, pp. 1-8
dc.descriptionHradil, Z., Quantum-state estimation (1997) Phys. Rev. A, 55, pp. R1561-R1564
dc.descriptionHradil, Z., Řeháček, J., Fiurášek, J., Ježek, M., Maximum-likelihood methods in quantum mechanics (2004) Lecture Notes in Physics, 649, pp. 163-172. , Quantum State Estimation, Springer, Berlin, Heidelberg
dc.descriptionJarre, F., An interior method for nonconvex semidefinite programs (2000) Optim. Eng., 1, pp. 347-372
dc.descriptionDe Klerk, E., (2002) Aspects of Semidefinite Programming: Interior Point Algorithms and Selected Applications, , Kluwer Academic Publishers, Dordrecht
dc.descriptionKočvara, M., Stingl, M., Pennon: A code for convex nonlinear and semidefinite programming (2003) Optim. Methods Softw, 18, pp. 317-333
dc.descriptionMaciel, T.O., Vianna, R.O., Optimal estimation of quantum processes using incomplete information: Variational quantum process tomography (2012) Quantum Inf. Comput, 12, pp. 442-447
dc.descriptionMeyer, C., (2001) Matrix Analysis and Applied Linear Algebra, , SIAM, Philadelphia
dc.descriptionMichelot, C., A finite algorithm for finding the projection of a point onto the canonical simplex of ℝn (1986) J. Optimiz. Theory Appl., 50, pp. 195-200
dc.descriptionVon Neumann, J., Functional Operators Vol. II. The Geometry of Orthogonal Spaces (1950) Annals of Mathematical Studies, 22. , Princeton University Press, Princeton
dc.descriptionNielsen, M., Chuang, I., Quantum Computation and Quantum Information (2004) Cambridge Series on Information and the Natural Sciences, , Cambridge University Press, Cambridge
dc.descriptionNocedal, J., Wright, S.J., (1999) Numerical Optimization, , Springer-Verlag, New York
dc.descriptionParis, M., Rehácek, J., Quantum State Estimation (2004) Lecture Notes in Physics, 649. , Springer, Berlin, Heidelberg
dc.descriptionPeres, A., Separability criterion for density matrices (1996) Phys. Rev. Lett., 77, pp. 1413-1415
dc.descriptionRaydan, M., On the Barzilai and Borwein choice of steplength for the gradient method (1993) IMA J. Numer. Anal, 13, pp. 321-326
dc.descriptionRehacek, J., Hradil, Z., Maximum Entropy Assisted Maximum Likelihood Inversion (2005) Conference on Lasers and Electro-Optics Europe, 2005 (CLEO/Europe 2005), p. 466
dc.descriptionŘeháček, J., Hradil, Z., Knill, E., Lvovsky, A.I., Diluted maximum-likelihood algorithm for quantum tomography (2007) Phys. Rev. A, 75
dc.descriptionRenes, J.M., Blume-Kohout, R., Scott, A.J., Caves, C.M., Symmetric informationally complete quantum measurements (2004) J. Math. Phys., 45, pp. 2171-2180
dc.descriptionRockafellar, R.T., (1970) Convex Analysis, , Princeton University Press, Princeton
dc.descriptionSturm, J.F., (1998) Using SeDuMi 1.02, A Matlab Toolbox for Optimization Over Symmetric Cones
dc.descriptionTeo, Y.S., Stoklasa, B., Englert, B.G., Rehacek, J., Hradil, Z., Incomplete quantum state estimation: A comprehensive study (2012) Phys. Rev. A, 85
dc.descriptionTodd, M., Semidefinite optimization (2001) Acta Numer., 10, pp. 515-560
dc.descriptionToh, K., Todd, M., Tutuncu, R., SDPT3 - A Matlab software package for semidefinite programming (1999) Optim. Methods Softw, pp. 545-581
dc.descriptionToh, K.C., An inexact primal-dual path following algorithm for convex quadratic SDP (2008) Math. Program, 112, pp. 221-254
dc.descriptionToh, K.C., Tucuntu, R.H., Todd, M.J., Inexact primal-dual path-following algorithms for a special class of convex quadratic sdp and related problems (2007) Pac. J. Optim, 3, pp. 135-164
dc.descriptionVandenberghe, L., Boyd, S., Semidefinite programming (1996) SIAM Rev., 38, pp. 49-95
dc.descriptionWolfe, P., Finding the nearest point in a polytope (1976) Math. Program, 11, pp. 128-149
dc.descriptionYamashita, H., Yabe, H., Harada, K., A primal-dual interior point method for nonlinear semidefinite programming (2012) Math. Program, 135, pp. 89-121
dc.descriptionZhang, H., Hager, W.W., A nonmonotone line search technique and its application to unconstrained optimization (2004) SIAM J. Optimiz, 14, pp. 1043-1056
dc.descriptionZyczkowski, K., Bengtsson, I., (2006) Geometry of Quantum States, , Cambridge University Press, New York
dc.descriptionZyczkowski, K., Horodecki, P., Sanpera, A., Lewenstein, M., Volume of the set of separable states (1998) Phys. Rev. A, 58, pp. 883-892
dc.descriptionZyczkowski, K., Penson, K.A., Nechita, I., Collins, B., Generating random density matrices (2011) J. Math. Phys., 52
dc.description
dc.description
dc.languageen
dc.publisherTaylor and Francis Ltd.
dc.relationOptimization Methods and Software
dc.rightsfechado
dc.sourceScopus
dc.titleA Projected Gradient Method For Optimization Over Density Matrices
dc.typeArtículos de revistas


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