While online convex optimization has emerged as a powerful large scale optimization approach, much of existing literature assumes a simple way to project onto a given feasible set. The assumption is often not true, and the projection step usually becomes the key computational bottleneck. Motivated by applications in risk-adjusted portfolio selection, in this paper we consider online quadratical...

Recently, several optimization approaches for portfolio selection have been proposed in order to alleviate the estimation error in the optimal portfolio. Among such are the normconstrained variance minimization and the robust portfolio models. In this paper, we examine the role of the norm constraint in the portfolio optimization from several directions. First, it is shown that the norm constra...

In this paper, we propose a multi-objective evolutionary metaheuristic approach based on the Pareto Ant Colony Optimization (P-ACO) and non-dominated genetic sorting algorithms (NSGA II NSGA III) to solve bi-objective portfolio optimization problem. P-ACO is used select best assets composing efficient portfolio. Then, III are separately find proportional weights of budget allocated selected The...

The relevant literature showed that many heuristic techniques have been investigated for constrained portfolio optimization problem but none of these studies presents multi-objective Scatter Search approach. In this work, we present a hybrid multi-objective population-based evolutionary algorithm based on Scatter Search with an external archive to solve the constrained portfolio selection probl...

We study in this paper the strong duality for discrete-time convex constrained portfolio selection problems when adopting a risk neutral computational approach. In contrast to the continuous-time models, there is no known result of the existence conditions in discrete-time models to ensure the strong duality. Investigating the relationship among the primal problem, the Lagrangian dual and the P...

Algencan is a freely available piece of software that aims to solve smooth large-scale constrained optimization problems. When applied to specific problems, obtaining a good performance in terms of efficacy and efficiency may depend on careful choices of options and parameters. In the present paper the application of Algencan to four portfolio optimization problems is discussed and numerical re...

In this paper we deal with linear chance-constrained optimization problems, a class of problems which naturally arise in practical applications in finance, engineering, transportation and scheduling, where decisions are made in presence of uncertainty. After giving the deterministic equivalent formulation of a linear chance-constrained optimization problem we construct a conjugate dual problem ...

Several attempts have been made to reduce the impact of estimation errors on the optimal portfolio composition. On the one hand, improved estimators of the necessary moments have been developed and on the other hand, heuristic methods have been generated to enhance the portfolio performance, for instance the "resampled efficiency" of Michaud (1998). We compare the out-ofsample performance of tr...