( Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled w...

A new approach for convolutive blind source separation (BSS) using penalty functions is proposed in this paper. Motivated by nonlinear programming techniques for the constrained optimization problem, it converts the convolutive BSS into a joint diagonalization problem with unconstrained optimization. Theoretical analyses together with numerical evaluations reveal that the proposed method not on...

This study covers the output-feedback model predictive control (MPC) of nonlinear systems subjected to stochastic disturbances and state chance constraints. The optimal problem is solved in a dynamic programming fashion, performed with extended Kalman filter. information summarized as Gaussian belief model. Thus, Bellman equation transformed into deterministic using this resulting constrained p...

In this paper we give a global convergence analysis of a basic version of an SQP algorithm described in 2] for the solution of large scale nonlinear inequality-constrained optimization problems. Several procedures and options have been added to the basic algorithm to improve the practical performance; some of these are also analyzed. The important features of the algorithm include the use of a ...

We present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step Q-superlinear convergence. The approach is based on an adaptive structured scheme due to Mahdavi-Amiri and Bartels of the exact penalty method of Coleman and Conn for nonlinearly constrained optimization problems. The structured adaptation also makes use of the ideas of N...

This paper concerns the second-order generalized differentiation theory of variational analysis and new applications of this theory to some problems of constrained optimization in finitedimensional spaces. The main focus is the so-called (full and partial) second-order subdifferentials of extended-real-valued functions, which are dual-type constructions generated by coderivatives of first-order...

In this paper, we reformulate the optimization in discriminative training (DT) of HMMs as an ellipsoid constrained quadratic programming (ECQP) problem, where a second order of the nonlinear space is adopted. We show that the unique optimal solution of ECQP can be obtained by an efficient line search and no relaxation is needed as in a general quadratically constrained quadratic programming (QC...

Nonlinear vehicle control allocation is achieved by distributing the control task to tire forces with nonlinear saturation constraints. The overall vehicle control is accomplished by developing a hierarchical scheme. First, a high-level sliding mode control with adaptive gain is considered to obtain the body force/moment for stable vehicle motion. The proposed controller only requires online ad...

In this paper, a regulation control problem for constrained nonlinear systems with quantized inputs is formulated as a Model Predictive Control (MPC) problem. The MPC problem is represented as a multi-parametric Nonlinear Integer Programming (mp-NIP) problem and a computational method to find an explicit approximate solution of this problem is considered. It consists in constructing a feasible ...

The systematic design of exact optimal designs experiments is typically challenging, as it results in nonconvex optimization problems. literature on the computation model-based via mathematical programming, when covariates are categorical variables, still scarce. We propose mixed-integer semidefinite programming formulations, to find D-, A- and I-optimal for linear models, locally nonlinear mod...