A class of generalized filled functions for unconstrained global optimization.
A class of nonmonotone stabilization methods in unconstrained optimization.
A compact variable metric algorithm for linearly constrained nonlinear minimax approximation
A comparative study of three different mathematical methods for solving the unit commitment problem.
A comparative study on optimization methods for the constrained nonlinear programming problems.
A conjugate gradient method for unconstrained optimization problems.
A conjugate gradient method with quasi-Newton approximation
The conjugate gradient method of Liu and Storey is an efficient minimization algorithm which uses second derivatives information, without saving matrices, by finite difference approximation. It is shown that the finite difference scheme can be removed by using a quasi-Newton approximation for computing a search direction, without loss of convergence. A conjugate gradient method based on BFGS approximation is proposed and compared with existing methods of the same class.
A conjugate gradient method with sufficient descent and global convergence for unconstrained nonlinear optimization.
A Constraint Linearization Method for Nondifferentiable Convex Minimization.
A contribution to methods of Exterior Penalty Functions for Nonlinear Programming
A convergence proof of a special version of the generalized reduced gradient method (GRGS)
A convergent algorithm for solving linear programs with an additional reverse convex constraint
A cooperative sensor network : optimal deployment and functioning
A network of mobile cooperative sensors is considered. The following problems are studied: (1) the “optimal“deployment of the sensors on a given territory; (2) the detection of local anomalies in the noisy data measured by the sensors. In absence of an information fusion center in the network, from “local” interactions between sensors “global“solutions of these problems are found.
A Cooperative Sensor Network: Optimal Deployment and Functioning
A network of mobile cooperative sensors is considered. The following problems are studied: (1) the “optimal" deployment of the sensors on a given territory; (2) the detection of local anomalies in the noisy data measured by the sensors. In absence of an information fusion center in the network, from “local" interactions between sensors “global" solutions of these problems are found.
A Discrete Projection Quasi Newton Method for Linearly Constrained Problems
A distributed search algorithm for global optimization on numerical spaces
A Dual Non-Linear Program.
A duality theorem for complex nonlinear programming
A family of model predictive control algorithms with artificial neural networks
This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used...
A filled function approach for nonsmooth constrained global optimization.