Current Issue : July - September Volume : 2017 Issue Number : 3 Articles : 6 Articles
We propose a new scalarization method which consists in constructing, for a\ngiven multiobjective optimization problem, a single scalarization function,\nwhose global minimum points are exactly vector critical points of the original\nproblem. This equivalence holds globally and enables one to use global optimization\nalgorithms (for example, classical genetic algorithms with ââ?¬Å?roulette\nwheelââ?¬Â selection) to produce multiple solutions of the multiobjective problem.\nIn this article we prove the mentioned equivalence and show that, if the ordering\ncone is polyhedral and the function being optimized is piecewise differentiable,\nthen computing the values of a scalarization function reduces to\nsolving a quadratic programming problem. We also present some preliminary\nnumerical results pertaining to this new method....
Information rate for discrete signaling constellations is significant. However, the computational complexity makes information rate\nrather difficult to analyze for arbitrary fadingmultiple-inputmultiple-output (MIMO) channels. An analytical method is proposed\nto compute information rate, which is characterized by considerable accuracy, reasonable complexity, and concise representation.\nThese features will improve accuracy for performance analysis with criterion of information rate....
In 2013, Bai and Zhang constructed modulus-based synchronous multisplitting methods\nfor linear complementarity problems and analyzed the corresponding convergence. In 2014, Zhang\nand Li studied the weaker convergence results based on linear complementarity problems. In 2008,\nZhang et al. presented global relaxed non-stationary multisplitting multi-parameter method by\nintroducing some parameters. In this paper, we extend Bai and Zhang�s algorithms and analyze global\nmodulus-based synchronous multisplitting multi-parameters TOR (two parameters overrelaxation)\nmethods. Moverover, the convergence of the corresponding algorithm in this paper are given when\nthe system matrix is an H+-matrix....
In this paper, an efficient computational approach is proposed to solve the\ndiscrete time nonlinear stochastic optimal control problem. For this purpose,\na linear quadratic regulator model, which is a linear dynamical system with\nthe quadratic criterion cost function, is employed. In our approach, the model-\nbased optimal control problem is reformulated into the input-output equations.\nIn this way, the Hankel matrix and the observability matrix are constructed.\nFurther, the sum squares of output error is defined. In these point of\nviews, the least squares optimization problem is introduced, so as the differences\nbetween the real output and the model output could be calculated. Applying\nthe first-order derivative to the sum squares of output error, the necessary\ncondition is then derived. After some algebraic manipulations, the optimal\ncontrol law is produced. By substituting this control policy into the input-\noutput equations, the model output is updated iteratively. For illustration,\nan example of the direct current and alternating current converter problem is\nstudied. As a result, the model output trajectory of the least squares solution is\nclose to the real output with the smallest sum squares of output error. In conclusion,\nthe efficiency and the accuracy of the approach proposed are highly\npresented....
The numerical solutions of linear integrodifferential equations of Volterra type have been considered. Power series is used as the\nbasis polynomial to approximate the solution of the problem. Furthermore, standard and Chebyshev-Gauss-Lobatto collocation\npoints were, respectively, chosen to collocate the approximate solution. Numerical experiments are performed on some sample\nproblems already solved by homotopy analysis method and finite difference methods. Comparison of the absolute error is obtained \nfrom the present method and those from aforementioned methods. It is also observed that the absolute errors obtained are very\nlow establishing convergence and computational efficiency....
This paper presents an upper bound for each of the generalized ...
Loading....