this paper presents a new meta-heuristic solution to find the efficient frontier using the mean-variance approach. portfolio optimization problem is a quadratic programming model and, changes to np-hard if the number of assets and constraints has increased, and it cannot be solved using common mathematical methods in a reasonable time. therefore, a heuristic or meta-heuristic algorithm should b...

The recently proposed low-complexity reduction-bycomposition least-mean-square (LMS) algorithm (RCLMS) costs only half multiplications compared to that of the conventional direct-form LMS algorithm (DLMS). This work intends to characterize its properties and conditions for mean and mean-square convergence. Closed-form mean-square error (MSE) as a function of the LMS step-size and an extra compe...

Designing a Least Mean Square (LMS) family adaptive algorithm includes solving the wellknown trade-off between the initial convergence speed and the mean-square error in steady state according to the requirements of the application at hands. The trade-off is controlled by the step-size parameter of the algorithm. Large step size leads to a fast initial conver‐ gence but the algorithm also exhib...

Adaptive filters are used in a number of applications, many of which can benefit from a reduction in power. In this paper we present derivations of the approximate expressions used in [1] for the increase in mean square error of the LMS adaptive algorithm when the total processing power is decreased.

A combination of two complex normalized least mean square (NLMS) adaptive filters that adapt on the same input signal at the same time is investigated. One of the filters has a large and the other one has a small step size. The outputs of the filters are combined together through a mixing parameter λ . This combination is an interesting new way of achieving simultaneously a fast initial converg...