This note points out an error in the local quadratic convergence proof of the predictorcorrector interior-point algorithm for solving the semidefinite linear complementarity problem based on the Alizadeh–Haeberly–Overton search direction presented in [M. Kojima, M. Shida, and S. Shindoh, SIAM J. Optim., 9 (1999), pp. 444–465]. Their algorithm is slightly modified and the local quadratic converg...

Abstract The accuracy of the extracted parameters is important for studying polyphase induction motor performance and/or control schemes. An investigated and improved interior search algorithm (IISA) presented in this study extracting optimal values estimated six-phase three-phase motors. This investigation was carried out on two motors as experimental research cases, utilizing features manufac...

in this work, harmony search meta-heuristic optimization algorithm has been developed for aerodynamic shape optimization problem for the first time. the aerodynamic shape, which has been investigated, is an airfoil with parces method for its shape parameterization. the problem was minimization of aerodynamic objective function using inverse design method and the objective function has been the ...

  We present a new full Nesterov and Todd step infeasible interior-point algorithm for semi-definite optimization. The algorithm decreases the duality gap and the feasibility residuals at the same rate. In the algorithm, we construct strictly feasible iterates for a sequence of perturbations of the given problem and its dual problem. Every main iteration of the algorithm consists of a feasibili...

wireless sensor networks are the new generation of networks that typically are formed great numbers of nodes and the communications of these nodes are done as wireless. the main goal of these networks is collecting data from neighboring environment of network sensors. since the sensor nodes are battery operated and there is no possibility of charging or replacing the batteries, the lifetime of ...

In this article, we propose a new class of search directions based on kernel function to solve the monotone semidefinite linear complementarity problem by primal-dual interior point algorithm. We show that algorithm benefits from best polynomial complexity, namely $ O(\sqrt{n}(\log n)^{2}\log\dfrac{n}{\epsilon})$. The implementation showed great improvement concerning time and number iterations.

The paper describes an interior–point algorithm for nonconvex nonlinear programming which is a direct extension of interior–point methods for linear and quadratic programming. Major modifications include a merit function and an altered search direction to ensure that a descent direction for the merit function is obtained. Preliminary numerical testing indicates that the method is robust. Furthe...

It is well known that each kernel function defines an interior-point algorithm. In this paper we propose new classes of kernel functions whose form is different from known kernel functions and define interior-point methods (IPMs) based on these functions whose barrier term is exponential power of exponential functions for P∗(κ )-horizontal linear complementarity problems (HLCPs). New search dir...

We present an infeasible-interior-point algorithm for monotone linear complementarity problems in which the search directions are affine scaling directions and the step lengths are obtained from simple formulae that ensure both global and superlinear convergence. By choosing the value of a parameter in appropriate ways, polynomial complexity and convergence with Q-order up to (but not including...