in traditional portfolio selection model coefficients often are certain and deterministic, but in real world these coefficients are probabilistic. so decision maker cannot estimate them exactly. financial optimization is one of the most attractive areas in decision under uncertainty. in the portfolio selection problem the decision maker considers simultaneously conflicting objectives such as ra...

We propose and analyze a pseudo-transient continuation algorithm for dynamics on subsets of R . Examples include certain flows on manifolds and the dynamic formulation of bound-constrained optimization problems. The method gets its global convergence properties from the dynamics and inherits its local convergence properties from any fast locally convergent iteration.

We suggest an algorithm for the calculation of large-amplitude traveling water waves in irrotational flows and in flows with constant and nonconstant vorticity, respectively. The algorithm is based on a quite general partial differential equation constrained optimization formulation, maximizing the wave amplitude subject to the PDE constraint that the Euler equations hold. Numerical examples il...

We establish a relationship between general constrained pseudoconvex optimization problems and globally projected dynamical systems. A corresponding novel neural network model, which is globally convergent and stable in the sense of Lyapunov, is proposed. Both theoretical and numerical approaches are considered. Numerical simulations for three constrained nonlinear optimization problems a...

A new high spectral accuracy compact difference scheme is proposed here. This has been obtained by constrained optimization of error in spectral space for discretizing first derivative for problems with non-periodic boundary condition. This produces a scheme with the highest spectral accuracy among all known compact schemes, although this is formally only second-order accurate. Solution of Navi...

We present a specific varying fitness function technique in genetic algorithm (GA) constrained optimization. This technique incorporates the problem's constraints into the fitness function in a dynamic way. It consists of forming a fitness function with varying penalty terms. The resulting varying fitness function facilitates the GA search. The performance of the technique is tested on two opti...