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Application of Advance Constrained Simplex Method for MIMO Systems in Quantum Communication Networks

Tomonobu Sato

Article ID: 1102
Vol 3, Issue 1, 2020, Article identifier:

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This paper first describes advanced constrained simplex method (advanced complex method), then it shows that this complex procedure has any problems when it’s taken for finding the maximum of a general nonlinear function of several variables within a constrained region is described in wireless communication systems, especially for multiple-input multiple-out (MIMO Configuration). Next advanced constrained simplex method is described how to resolve the problem of the multiple-input multiple-out, and shown how to be efficient compared with the complex method and the simplex method by some simulations. And this wireless network design can be used to MIMO systems in Quantum Communication networks. The feature of technology by which the system told by this paper can get optimum solution by a little search number of times compared with a conventional system in the MIMO environment with more than one optimal value, and is at the place. This system was applied to the Quantum network environment by this paper. This can achieve more compact than the conventional Quantum network environment.


Quantum Communication Networks; Application of Artificial Intelligence (AI); MIMO Systems

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Slonim N, Atwal GS, Tkacik G, Bialek W. Estimating mutual information and multi-information in large networks. Available from:

Boggs PT, Tolle JW. A family of descent functions for constrained optimization. SIAM J Numer Anal 1984; 21: 1146-1161.

Pirandola S. End-to-end capacities of a quantum communication network. Commun Phys 2019.

Boggs PT, Tolle JW. A strategy for global convergence in a sequential quadratic programming algorithm. SIAM J Numer Anal 1989; 600-623.

Celis MR, Dennis JE, Tapia RA. A trust region strategy for nonlinear equality constrained optimization. Numerical Optimization 1985; 71-82.

Chamberlain RM, Lemarechal C, Pedersen HC, et al. The watchdog technique for forcing convergence in algorithms for constrained optimization. Math, Program. Stud 1982; 16: 1-17.

Coleman T, Conn A. On the local convergence of a quasi-Newton method for the nonlinear programming problem. SIAM J Numer Anal 1984; 21: 755-769.

Pillo DG, Grippo L. A new class of augmented Lagrangians in nonlinear programming. SIAM J Control Opt 1979; 17: 618-628.

Fontecilla R. A general convergence theory for quasi-Newton methods for constrained optimization [PhD thesis]. Houston, Tex.: Mathematical Sciences Dept., Rice Univ.; 1983.

Glad ST. Properties of updating methods for the multiplies in augmented Lagrangiahs. J Optim Theor Appl 1979; 28: 135-156.

Han SP. Superlinearly convergent variable metric algorithms for general nonlinear programming problems. 1974.

Nocedal J, Overton ML. Projected Hessian updating algorithms for nonlinearly constrained optimization. SIAM J Numer Anal 1985; 22(5): 821-850.

Powell MJD. Algorithms for nonlinear constraints that use Lagrangian functions. Math Program 1978; 14: 224-248.

Powell MJD. The performance of two subroutines for constrained optimization on some difficult test problems. Numerical Optimization 1985; 160-177.

Powell MJD, Yuan Y. A recursive quadratic programming algorithm that uses differentiable exact penalty functions. Math, Program 1986; 35: 265-278.

Schittkowski K. The nonlinear programming method of Wilson, Han, and Powell with an augmented Lagrangian type line search function, Part I: Convergence analysis. Numer Math 1981; 38(1): 83-114.

Tapia RA. Diagonalized multiplier methods and quasi-Newton methods for constrained optimization. J Optim Theor Appl 1977; 22: 135-184.

Tapia RA. Quasi-Newton method for equality constraint optimization: Equivalence of existing methods and a new implementation. Nonlinear Programming 3. O. L. Mangasarian, R. R. Meyer, and S. M. Robinson, Eds. New York: Academic Press; 1978. p. 125-164.

Box MJ. A New method of constrained optimization and a comparison with other methods. Computer J 1965; 8: 42-52.

Kadloor S, Adve RS, Eckford AW. Molecular communication using brownian motion with drift. IEEE Transactions on Nano Bioscience 2012; 2: 89-99.

Sato T. Modeling and simulation in wireless sensor networks. Journal of Wireless Communications - Special Issue on Wireless Sensor Networks 2017.

Nemenman I, Shafee F, Bialek W. Entropy and inference, revisited. In: Dietterich TG, Becker S, Ghahramani Z (editors). Advances in neural information processing 14. Cambridge: MIT Press; 2002. p. 471–478.

Mille GA. Information theory in psychology: Problems and methods IIB. Quastler H (editors). Glencoe IL: Free Press; 1955. p. 95-100. See also Treves A,Panzeri S. Neural Comp 1995; 7: 399–407, and Paninski L. Neural Comp 2003; 15: 1191–1253.

Cover TM, Thomas JA. Elements of information theory. New York: John Wiley & Sons; 1991.

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