We define a regularized variant of the Dual Dynamic Programming algorithm called REDDP (REgularized Dual Dynamic Programming) to solve nonlinear dynamic programming equations. We extend the algorithm to solve nonlinear stochastic dynamic programming equations. The corresponding algorithm, called...

Many optimization problems that arise in practice can be reduced to the problem of computing the projection of a given vector in a Euclidean space onto the simplicial cone generated by a set of linearly independent vectors. For example, the well-known problem in finance of determining the...

A fast method based on coordinate-wise descent algorithms is developed to solve portfolio optimization problems in which asset weights are constrained by Lq norms for 1=q=2. The method is first applied to solve a minimum variance portfolio (mvp) optimization problem in which asset weights are...

The paper studies stochastic optimization of an intertemporal consumption model to allocate financial assets between risky and risk-free assets. We use a stochastic optimization technique, in which utility is maximized subject to a self-financing portfolio constraint. The papers in literature...

This paper extends the socially responsible multiobjective problem to (i) estimating optimal portfolios via reward/risk maximization, (ii) including dependence structure between asset returns using vine copulas, and (iii) incorporating enhanced indexation utilizing cumulative zero-order...

In this short note, we consider mean-variance optimized portfolios with transaction costs. We show that introducing quadratic transaction costs makes the optimization problem more difficult than using linear transaction costs. The reason lies in the specification of the budget constraint, which...

In this contribution we propose a dynamic tracking error problem and we consider the problem of monitoring at discrete point the shortfall of the portfolio below a set of given reference levels of wealth. We formulate and solve the resulting dynamic optimization problem using stochastic...

Portfolio selection problems have been thoroughly studied under the risk-and-return paradigm introduced by Markowitz. However, the usefulness of this approach has been hindered by some practical considerations that have resulted in poorly diversified portfolios or solutions that are extremely...

This work presents five convex reformulations of portfolio kurtosis that allows us to pose kurtosis as a parametric convex risk measure. These new reformulations are based on new formulas for estimation of portfolio moments and co-moments matrices, second order cone and semidefinite programming....

This work presents a disciplined convex programming framework for Kelly criterion in portfolio optimization based on exponential cone programming. This framework allows us to incorporate mean logarithmic return in problems like maximize mean logarithmic return subject to a risk constraint,...