points method of projective type for calculate the initial dual solution of the convex nonlinear programming using the Ye Lustig variant applied to linear programming, as a result we propose a modification in this algorithm to reduce the number of iterations and the computing time of this algorithm...
The paper is organized as follows: In Section 2, the PI/PID control design problem is stated and formulated as a non-convex constrained optimization problem. The common features of the equality constraints on the closed-loop stability, the maximum sensitivity, and maximum complementary sensitivity ...
In this paper we study the auxiliary problems that appear in p-order tensor methods for unconstrained minimization of convex functions with n-H枚lder continuous pth derivatives. This type of auxiliary problems corresponds to the minimization of a (p+n)-order regularization of the pth order ...
It has been shown that a convex solution to the problem can be obtained efficiently by incorporating an integrator into the filter structure. Sufficient conditions for the existence of a robust nonlinear filter are given in terms of the solvability of the PMIs, which have been formulated as SOS...
Kamboj VK, Bath SK, Dhillon JS (2015) Solution of non-con- vex economic load dispatch problem using grey wolf optimizer. Neural Comput Appl 27:1-16V. K. Kamboj, S. K. Bath, and J. S. Dhillon, "Solution of non- convex economic load dispatch problem using grey wolf optimizer," ...
Note that you need to callGRBsynceven if you know that the asynchronous optimization has already completed. Return value: A non-zero return value indicates that a problem occurred while solving the model. Refer to theError Codestable for a list of possible return values. Details on the error...
options(2) sets the maximum number of iterations before the optimization terminates. If options(2) = 0, then feasp defaults to a maximum of 100 iterations. options(3) sets the feasibility radius. The feasibility radius R is a constraint on the decision vector x = (x1, . . ., xN), su...
We provide conditions under which the solution set of a nonsmooth and nonconvex optimization problem is non-empty and/or compact. We also examine related properties such as the compactness of the sublevel sets, the boundedness from below and the coercivity of the objective function to characterize...
Exact Lipschitz Regularization of Convex Optimization Problems Article Open access 08 June 2024 A modified feasible semi-smooth asymptotically Newton method for nonlinear complementarity problems Article Open access 22 September 2016 References S.C. Billups and M.C. Ferris, QPCOMP: A quadratic pro...
The architectures are specifically based on stationary conditions pertaining to primal and dual variables in a class of generally nonconvex optimization problems. The stationary conditions, which are closely related to the principles of stationary content and co-content that naturally arise from Tellegen...