×

Re-examination of Bregman functions and new properties of their divergences. (English) Zbl 1407.52008

Summary: The Bregman divergence (Bregman distance, Bregman measure of distance) is a certain useful substitute for a distance, obtained from a well-chosen function (the ‘Bregman function’). Bregman functions and divergences have been extensively investigated during the last decades and have found applications in optimization, operations research, information theory, nonlinear analysis, machine learning and more. This paper re-examines various aspects related to the theory of Bregman functions and divergences. In particular, it presents many sufficient conditions which allow the construction of Bregman functions in a general setting and introduces new Bregman functions (such as a negative iterated log entropy). Moreover, it sheds new light on several known Bregman functions such as quadratic entropies, the negative Havrda-Charvát-Tsallis entropy, and the negative Boltzmann-Gibbs-Shannon entropy, and it shows that the negative Burg entropy, which is not a Bregman function according to the classical theory but nevertheless is known to have ‘Bregmanian properties’, can, by our re-examination of the theory, be considered as a Bregman function. Our analysis yields several by-products of independent interest such as the introduction of the concept of relative uniform convexity (a certain generalization of uniform convexity), new properties of uniformly and strongly convex functions, and results in Banach space theory.

MSC:

52A41 Convex functions and convex programs in convex geometry
52B55 Computational aspects related to convexity
46N10 Applications of functional analysis in optimization, convex analysis, mathematical programming, economics
90C25 Convex programming
90C30 Nonlinear programming
46T99 Nonlinear functional analysis
47N10 Applications of operator theory in optimization, convex analysis, mathematical programming, economics
49M37 Numerical methods based on nonlinear programming
26B25 Convexity of real functions of several variables, generalizations
58C05 Real-valued functions on manifolds

References:

[1] Bregman, Lm., The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming, Comput Math Math Phys, 7, 200-217 (1967) · Zbl 0186.23807 · doi:10.1016/0041-5553(67)90040-7
[2] Censor, Y.; Lent, A., An iterative row-action method for interval convex programming, J Optim Theory Appl, 34, 321-353 (1981) · Zbl 0431.49042 · doi:10.1007/BF00934676
[3] Banerjee, A.; Guo, X.; Wang, H., On the optimality of conditional expectation as a Bregman predictor, IEEE Trans Inf Theory, 51, 2664-2669 (2005) · Zbl 1284.94025 · doi:10.1109/TIT.2005.850145
[4] Banerjee, A.; Merugu, S.; Dhillon, Is, Clustering with Bregman divergences, J Mach Learn Res, 6, 1705-1749 (2005) · Zbl 1190.62117
[5] Bauschke, Hh; Borwein, Jm; Combettes, Pl., Essential smoothness, essential strict convexity, and Legendre functions in Banach spaces, Commun Contemp Math, 3, 615-647 (2001) · Zbl 1032.49025 · doi:10.1142/S0219199701000524
[6] Bauschke, Hh; Borwein, Jm; Combettes, Pl., Bregman monotone optimization algorithms, SIAM J Control Optim, 42, 596-636 (2003) · Zbl 1049.90053 · doi:10.1137/S0363012902407120
[7] Bauschke, Hh; Macklem, Ms; Sewell, Jb, Klee sets and Chebyshev centers for the right Bregman distance, J Approx Theory, 162, 1225-1244 (2010) · Zbl 1200.52006 · doi:10.1016/j.jat.2010.01.001
[8] Beck, A.; Teboulle, M., Mirror descent and nonlinear projected subgradient methods for convex optimization, Oper Res Lett, 31, 167-175 (2003) · Zbl 1046.90057 · doi:10.1016/S0167-6377(02)00231-6
[9] Boissonnat, J-D; Nielsen, F.; Nock, R., Bregman Voronoi diagrams, Discrete Comput Geom, 44, 281-307 (2010) · Zbl 1201.52020 · doi:10.1007/s00454-010-9256-1
[10] Borwein, Jm; Reich, S.; Sabach, S., A characterization of Bregman firmly nonexpansive operators using a new monotonicity concept, J Nonlinear Convex Anal, 12, 161-184 (2011) · Zbl 1221.26019
[11] Bregman, Lm; Censor, Y.; Reich, S., Dykstra’s algorithm as the nonlinear extension of Bregman’s optimization method, J Convex Anal, 6, 319-333 (1999) · Zbl 0960.90067
[12] Burachik, Rs; Scheimberg, S., A proximal point method for the variational inequality problem in Banach spaces, SIAM J Control Optim, 39, 1633-1649 (2000) · Zbl 0988.90045 · doi:10.1137/S0363012998339745
[13] Butnariu, D.; Iusem, An., Totally convex functions for fixed point computation and infinite dimensional optimization (2000), Dordrecht, The Netherlands: Kluwer Academic Publishers, Dordrecht, The Netherlands · Zbl 0960.90092
[14] Butnariu, D.; Reich, S.; Zaslavski, Aj., Asymptotic behavior of relatively nonexpansive operators in Banach spaces, J Appl Anal, 7, 151-174 (2001) · Zbl 1010.47032 · doi:10.1515/JAA.2001.151
[15] Butnariu, D, Reich, S, Zaslavski, Aj.Generic power convergence of nonlinear operators in Banach spaces. In: Cho YJ, Kim JK, Kang SM, editors. Fixed point theory and applications (Chinju/Masan, 2001). Hauppauge, NY: Nova Sci. Publ.; 2003. p. 35-49. · Zbl 1091.47054
[16] Butnariu, D, Reich, S, Zaslavski, Aj.Convergence to fixed points of inexact orbits of Bregman-monotone and of nonexpansive operators in Banach spaces. In: H. F. Nathansky et al., editor. Fixed point theory and its applications. Yokohama: Yokohama Publ.; 2006. p. 11-32. · Zbl 1116.47052
[17] Butnariu, D.; Resmerita, E., Bregman distances, totally convex functions, and a method for solving operator equations in Banach spaces, Abstr Appl Anal, 2006, 1-39 (2006) · Zbl 1130.47046 · doi:10.1155/AAA/2006/84919
[18] Cayton, L.Fast nearest neighbor retrieval for Bregman divergences. Proceedings of the 25th International Conference on Machine Learning (ICML). Helsinki: IEEE; 2008. p. 112-119.
[19] Censor, Y.; De Pierro, Ar; Iusem, An., Optimization of Burg’s entropy over linear constraints, Appl Numer Math, 7, 151-165 (1991) · Zbl 0722.65032 · doi:10.1016/0168-9274(91)90059-9
[20] Censor, Y.; Iusem, An; Zenios, Sa., An interior point method with Bregman functions for the variational inequality problem with paramonotone operators, Math Program (Ser A), 81, 373-400 (1998) · Zbl 0919.90123
[21] Censor, Y.; Reich, S., Iterations of paracontractions and firmly nonexpansive operators with applications to feasibility and optimization, Optimization, 37, 323-339 (1996) · Zbl 0883.47063 · doi:10.1080/02331939608844225
[22] Censor, Y.; Zenios, As., Parallel optimization: theory, algorithms, and applications (1997), New York: Oxford University Press, New York · Zbl 0945.90064
[23] Chen, G.; Teboulle, M., Convergence analysis of a proximal-like minimization algorithm using Bregman functions, SIAM J Optim, 3, 538-543 (1993) · Zbl 0808.90103 · doi:10.1137/0803026
[24] Cichocki, A.; Amari, S-I., Families of alpha- beta- and gamma-divergences: flexible and robust measures of similarities, Entropy, 12, 1532-1568 (2010) · Zbl 1229.94030 · doi:10.3390/e12061532
[25] Collins, M.; Schapire, Re; Singer, Y., Logistic regression, AdaBoost and Bregman distances, Mach Learn, 48, 253-285 (2002) · Zbl 0998.68123 · doi:10.1023/A:1013912006537
[26] Csiszár, I., Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems, Ann Statist, 19, 2032-2066 (1991) · Zbl 0753.62003 · doi:10.1214/aos/1176348385
[27] De Pierro, Ar.Multiplicative iterative methods in computed tomography. In: Herman GT, Louis AK, Natterer F, editors. Mathematical methods in tomography (Oberwolfach, 1990). Berlin: Springer; 1991. p. 167-186. (Lecture notes in math.; 1497). · Zbl 0762.65036
[28] De Pierro, Ar; Iusem, An., A relaxed version of Bregman’s method for convex programming, J Optim Theory Appl, 51, 421-440 (1986) · Zbl 0581.90069 · doi:10.1007/BF00940283
[29] Eckstein, J., Approximate iterations in Bregman-function-based proximal algorithms, Math Program (Ser A), 83, 113-123 (1998) · Zbl 0920.90117
[30] Elfving, T., An algorithm for maximum entropy image reconstruction from noisy data, Math Comput Model, 12, 729-745 (1989) · Zbl 0691.68102 · doi:10.1016/0895-7177(89)90358-0
[31] Gupta, Md, Huang, Ts.Bregman distance to L1 regularized logistic regression. In: International Conference on Pattern Recognition (ICPR). Tampa, FL, USA: IEEE; 2008. p. 1-4.
[32] Jones, Lk; Byrne, Cl., General entropy criteria for inverse problems, with applications to data compression, pattern classification, and cluster analysis, IEEE Trans Inf Theory, 36, 23-30 (1990) · Zbl 0731.62016 · doi:10.1109/18.50370
[33] Kaplan, A.; Tichatschke, R., On inexact generalized proximal methods with a weakened error tolerance criterion, Optimization, 53, 3-17 (2004) · Zbl 1068.65064 · doi:10.1080/02331930410001661217
[34] Kiwiel, K., Proximal minimization methods with generalized Bregman functions, SIAM J Control Optim, 35, 1142-1168 (1997) · Zbl 0890.65061 · doi:10.1137/S0363012995281742
[35] Murata, N.; Takenouchi, T.; Kanamori, T., Information geometry of U-Boost and Bregman divergence, Neural Comput, 16, 1437-1481 (2004) · Zbl 1102.68489 · doi:10.1162/089976604323057452
[36] Reich, S.; Sabach, S., Two strong convergence theorems for a proximal method in reflexive Banach spaces, Numer Funct Anal Optim, 31, 22-44 (2010) · Zbl 1200.47085 · doi:10.1080/01630560903499852
[37] Reich, S.; Sabach, S., Two strong convergence theorems for Bregman strongly nonexpansive operators in reflexive Banach spaces, Nonlinear Anal, 73, 122-135 (2010) · Zbl 1226.47089 · doi:10.1016/j.na.2010.03.005
[38] Taskar, B.; Lacoste-Julien, S.; Jordan, Mi., Structured prediction, dual extragradient and Bregman projections, J Mach Learn Res, 7, 1627-1653 (2006) · Zbl 1222.62143
[39] Teboulle, M., Entropic proximal mappings with applications to nonlinear programming, Math Oper Res, 17, 670-690 (1992) · Zbl 0766.90071 · doi:10.1287/moor.17.3.670
[40] Teboulle, M., A unified continuous optimization framework for center-based clustering methods, J Mach Learn Res, 8, 65-102 (2007) · Zbl 1222.68318
[41] Yin, W.; Osher, S.; Goldfarb, D., Bregman iterative algorithms for \(####\)-minimization with applications to compressed sensing, SIAM J Imaging Sci, 1, 143-168 (2008) · Zbl 1203.90153 · doi:10.1137/070703983
[42] Zaslavski, Aj., Convergence of a proximal-like algorithm in the presence of computational errors, Taiwanese J Math, 14, 2307-2328 (2010) · Zbl 1237.49043 · doi:10.11650/twjm/1500406077
[43] Reem, D, Reich, S, De Pierro, A.BISTA: a Bregmanian proximal gradient method without the global Lipschitz continuity assumption. arXiv:1804.10273 [math.OC] (preprint, 2018; current version: [v1], 19 Apr 2018). · Zbl 1427.90222
[44] Brezis, H., Functional analysis, Sobolev spaces and partial differential equations (2011), New York: Springer, New York · Zbl 1220.46002
[45] Kreyszig, E., Introductory functional analysis with applications (1978), New York-London-Sydney: John Wiley & Sons, New York-London-Sydney · Zbl 0368.46014
[46] Ambrosetti, A.; Prodi, G., A primer of nonlinear analysis (1993), New York, NY: Cambridge University Press, New York, NY · Zbl 0781.47046
[47] Bauschke, Hh; Combettes, Pl., Convex analysis and monotone operator theory in Hilbert spaces (2017), Cham, Switzerland: Springer International Publishing, Cham, Switzerland · Zbl 1359.26003
[48] Rockafellar, Rt., Convex analysis, 28 (1970), Princeton, NJ: Princeton University Press, Princeton, NJ · Zbl 0193.18401
[49] Vladimirov, Aa; Nesterov, Ye; Chekanov, Yn., Uniformly convex functionals, Vestnik Moskov Univ Ser XV Vychisl Mat Kibernet, 3, 12-23 (1978) · Zbl 0442.47046
[50] Nesterov, Y., Introductory lectures on convex optimization: a basic course, 87 (2004), Boston: Kluwer Academic Publishers, Boston · Zbl 1086.90045
[51] Zălinescu, C., On uniformly convex functions, J Math Anal Appl, 95, 344-374 (1983) · Zbl 0519.49010 · doi:10.1016/0022-247X(83)90112-9
[52] Zălinescu, C., Convex analysis in general vector spaces (2002), River Edge, NJ: World Scientific Publishing, River Edge, NJ · Zbl 1023.46003
[53] Butnariu, D.; Iusem, An; Zălinescu, C., On uniform convexity, total convexity and convergence of the proximal point and outer Bregman projection algorithms in Banach spaces, J Convex Anal, 10, 35-61 (2003) · Zbl 1091.90078
[54] Borwein, J.; Guirao, Aj; Hájek, P., Uniformly convex functions on Banach spaces, Proc Amer Math Soc, 137, 1081-1091 (2009) · Zbl 1184.52009 · doi:10.1090/S0002-9939-08-09630-5
[55] Borwein, Jm; Vanderwerff, J., Constructions of uniformly convex functions, Canad Math Bull, 55, 697-707 (2012) · Zbl 1260.46009 · doi:10.4153/CMB-2011-049-2
[56] Csiszár, I.; Tusnády, G., Information geometry and alternating minimization procedures, Statist Decisions Suppl, 1, 205-237 (1984) · Zbl 0547.60004
[57] Bauschke, Hh; Borwein, Jm., Legendre functions and the method of random Bregman projections, J Convex Anal, 4, 27-67 (1997) · Zbl 0894.49019
[58] Reem, D.The Bregman distance without the Bregman function II. In: Reich S, Zaslavski AJ, editors. Optimization theory and related topics. Providence, RI: Amer. Math. Soc.; 2012. p. 213-223. (Contemp. Math.; 568). · Zbl 1290.47058
[59] Reich, S.A weak convergence theorem for the alternating method with Bregman distances. In: Kartsatos AG, editor. Theory and applications of nonlinear operators of accretive and monotone type. New York: Dekker; 1996. p. 313-318. (Lecture notes in pure and appl. math.; 178). · Zbl 0943.47040
[60] Bauschke, Hh; Combettes, Pl., Construction of best Bregman approximations in reflexive Banach spaces, Proc Amer Math Soc, 131, 3757-3766 (2003) · Zbl 1040.41016 · doi:10.1090/S0002-9939-03-07050-3
[61] Butnariu, D.; Byrne, C.; Censor, Y., Redundant axioms in the definition of Bregman functions, J Convex Anal, 10, 245-254 (2003) · Zbl 1091.90077
[62] Phelps, Rr., Convex functions, monotone operators and differentiability, 1364 (1993), Berlin: Springer-Verlag, Berlin · Zbl 0921.46039
[63] Alber, Y.; Butnariu, D., Convergence of Bregman projection methods for solving consistent convex feasibility problems in reflexive Banach spaces, J Optim Theory Appl, 92, 33-61 (1997) · Zbl 0886.90179 · doi:10.1023/A:1022631928592
[64] Van Tiel, J., Convex analysis: an introductory text (1984), Belfast, Northern Ireland: John Wiley and Sons, Universities Press, Belfast, Northern Ireland · Zbl 0565.49001
[65] Solodov, Mv; Svaiter, Bf., An inexact hybrid generalized proximal point algorithm and some new results on the theory of Bregman functions, Math Oper Res, 51, 214-230 (2000) · Zbl 0980.90097 · doi:10.1287/moor.25.2.214.12222
[66] Araujo, A., The non-existence of smooth demand in general Banach spaces, J Math Econ, 17, 309-319 (1988) · Zbl 0666.90004 · doi:10.1016/0304-4068(88)90014-6
[67] Gohberg, I.; Goldberg, S., Basic operator theory (1981), Boston, MA: Birkhäuser, Boston, MA · Zbl 0458.47001
[68] Beauzamy, B., Introduction to Banach spaces and their geometry, 68 (1982), Amsterdam-New York: North-Holland Publishing Co., Amsterdam-New York · Zbl 0491.46014
[69] Dunford, N.; Schwartz, Jt., Linear operators. I. General theory, 7 (1958), New York, London: Interscience Publishers, Inc., New York, London · Zbl 0084.10402
[70] Gibbs, Jw., On the equilibrium of heterogeneous substances (first part), Trans Conn Acad Arts Sci, 3, 108-248 (18741878) · JFM 08.0559.03
[71] Shannon, Ce., A mathematical theory of communication, Bell Syst Tech J, 27, 379-423 (1948) · Zbl 1154.94303 · doi:10.1002/j.1538-7305.1948.tb01338.x
[72] Wiener, N., Cybernetics, or control and communication in the animal and the machine, 1053 (1948), Paris: Hermann et Cie., Paris · Zbl 0155.27901
[73] Censor, Y, De Pierro, Ar, Elfving, T, et al. On iterative methods for linearly constrained entropy maximization. In: Wakulicz A, editor. Numerical analysis and mathematical modelling. PWN: Warsaw; 1990. p. 145-163. (Banach Center Publ; 24). · Zbl 0718.65047
[74] Kullback, S.; Leibler, Ra., On information and sufficiency, Ann Math Statist, 22, 79-86 (1951) · Zbl 0042.38403 · doi:10.1214/aoms/1177729694
[75] Havrda, J.; Charvát, F., Quantification method of classification processes. Concept of structural a-entropy, Kybernetika, 3, 30-35 (1967) · Zbl 0178.22401
[76] Tsallis, C., Possible generalization of Boltzmann-Gibbs statistics, J Statist Phys, 52, 479-487 (1988) · Zbl 1082.82501 · doi:10.1007/BF01016429
[77] Borland, L.; Plastino, Ar; Tsallis, C., Information gain within nonextensive thermostatistics, J Math Phys, 39, 6490-6501 (1998) · Zbl 0938.82001 · doi:10.1063/1.532660
[78] Pickup, Rm; Cywinski, R.; Pappas, C., Generalized spin-glass relaxation, Phys Rev Lett, 102 (2009) · doi:10.1103/PhysRevLett.102.097202
[79] Gao, Y.; Liu, W., BeTrust: a dynamic trust model based on Bayesian inference and Tsallis entropy for medical sensor networks, J Sensors, 2014 (2014) · doi:10.1155/2014/649392
[80] Li, T.; Zhou, M., ECG classification using wavelet packet entropy and random forests, Entropy, 18 (2016) · doi:10.3390/e18080285
[81] Abe, S.; Okamoto, Y., Nonextensive statistical mechanics and its applications, 560 (2001), Berlin: Springer-Verlag, Berlin · Zbl 0979.00041
[82] Gell-Mann, M.; Tsallis, C., Nonextensive entropy—interdisciplinary applications (2004), New York: Oxford University Press, New York · Zbl 1127.82004
[83] Manic, Ks; Priya, Rk; Rajinikanth, V., Image multithresholding based on Kapur/Tsallis entropy and firefly algorithm, Indian J Sci Technol, 9 (2016)
[84] Tsallis, C., Introduction to nonextensive statistical mechanics: approaching a complex world (2009), New York: Springer, New York · Zbl 1172.82004
[85] Tsallis, C.Nonextensive statistical mechanics and thermodynamics: bibliography, 2018. This is a continuously updated online list: . Retrieved version: 16 October 2018 (with 6913 bibliographic items). A dedicated website (with more items) is: .
[86] Lafferty, J.Additive models, boosting, and inference for generalized divergences. Proceedings of the Twelfth Annual Conference on Computational Learning Theory (Santa Cruz, California, USA, 1999), COLT ’99. ACM; p. 125-133.
[87] Cruz Neto, Jx; Ferreira, Op; Iusem, An, Dual convergence of the proximal point method with Bregman distances for linear programming, Optim Methods Softw, 22, 339-360 (2007) · Zbl 1133.90007 · doi:10.1080/10556780600565083
[88] Nguyen, Qv., Forward-backward splitting with Bregman distances, Vietnam J Math, 45, 519-539 (2017) · Zbl 1371.90106 · doi:10.1007/s10013-016-0238-3
[89] Bauschke, Hh; Bolte, J.; Teboulle, M., A descent lemma beyond Lipschitz gradient continuity: first-order methods revisited and applications, Math Oper Res, 42, 330-348 (2017) · Zbl 1364.90251 · doi:10.1287/moor.2016.0817
[90] Burg, Jp.Maximum entropy spectral analysis. Paper Presented at the 37th Meeting of the Society of Exploration Geophysicists (SEG); 1967; Oklahoma City, Oklahoma, USA.
[91] Burg, Jp.Maximum entropy spectral analysis [PhD thesis]. Stanford University, CA, USA; 1975. Available from: .
[92] Edward, J.; Fitelson, M., Notes on maximum-entropy processing (corresp.), IEEE Trans Inf Theory, 19, 232-234 (1973) · Zbl 0252.62048 · doi:10.1109/TIT.1973.1054965
[93] Frieden, Br.Image enhancement and restoration. In: Huang TS, editor. Picture processing and digital filtering. Berlin, HL: Springer Berlin Heidelberg; 1975. p. 177-248.
[94] Itakura, F, Saito, S.Analysis synthesis telephony based on the maximum likelihood method. In: Kohasi Y, editor. Reports of the 6th International Congress on Acoustics, Tokyo. 1968. p. C17-C20.
[95] Censor, Y.; Lent, A., Optimization of ‘\(####\)’ entropy over linear equality constraints, SIAM J Control Optim, 25, 921-933 (1987) · Zbl 0631.90050 · doi:10.1137/0325050
[96] Jones, Lk; Trutzer, V., Computationally feasible high-resolution minimum-distance procedures which extend the maximum-entropy method, Inverse Probl, 5, 749-766 (1989) · Zbl 0701.62016 · doi:10.1088/0266-5611/5/5/006
[97] Burachik, Rs; Iusem, An., A generalized proximal point algorithm for the variational inequality problem in a Hilbert space, SIAM J Optim, 8, 197-216 (1998) · Zbl 0911.90273 · doi:10.1137/S1052623495286302
[98] Burachik, Rs; Iusem, An; Svaiter, Bf., Enlargement of monotone operators with applications to variational inequalities, Set-Valued Anal, 5, 159-180 (1997) · Zbl 0882.90105 · doi:10.1023/A:1008615624787
[99] Iusem, An., Some properties of generalized proximal point methods for quadratic and linear programming, J Optim Theory Appl, 85, 593-612 (1995) · Zbl 0831.90092 · doi:10.1007/BF02193058
[100] Iusem, An; Monteiro, Rdc., On dual convergence of the generalized proximal point method with Bregman distances, Math Oper Res, 25, 606-624 (2000) · Zbl 0980.90064 · doi:10.1287/moor.25.4.606.12110
[101] Silva, Pjs; Eckstein, J.; Humes Jr, C., Rescaling and stepsize selection in proximal methods using separable generalized distances, SIAM J Optim, 12, 238-261 (2001) · Zbl 1039.90053 · doi:10.1137/S1052623499365784
[102] Ben-Tal, A.; Margalit, T.; Nemirovski, A., The ordered subsets mirror descent optimization method with applications to tomography, SIAM J Optim, 12, 79-108 (2001) · Zbl 0992.92034 · doi:10.1137/S1052623499354564
[103] Nemirovsky, As; Yudin, Db., Problem complexity and method efficiency in optimization (1983), New York: John Wiley & Sons, Inc., New York · Zbl 0501.90062
[104] Reem, D.; Reich, S., Solutions to inexact resolvent inclusion problems with applications to nonlinear analysis and optimization, Rend Circ Mat Palermo (2), 67, 337-371 (2018) · Zbl 1401.90233
[105] Prus, S.Geometrical background of metric fixed point theory. In: Kirk WA, Sims B, editors. Handbook of metric fixed point theory. Dordrecht: Kluwer Acad. Publ.; 2001. p. 93-132. · Zbl 1018.46010
[106] Reem, D.; Reich, S., Fixed points of polarity type operators, J Math Anal Appl, 467, 1208-1232 (2018) · Zbl 1500.47082 · doi:10.1016/j.jmaa.2018.07.057
[107] Lax, Pd, Milgram, An.Parabolic equations. In: Bers L, Bochner S, John F, editors. Contributions to the theory of partial differential equations. Princeton, N. J.: Princeton University Press; 1954. p. 167-190. (Annals of mathematics studies; 33). · Zbl 0058.08703
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.