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...

Due to the reason that the randomness of the parameters in the MASK algorithm always leads to the volatility and uncertainty of the mining results, this paper proposed an optimization algorithm for the maximum likelihood estimation of the parameters to choose a parameter that is most approximate to the common parameters from the parameter group that has been generated randomly. Such a parameter...

This paper describes a portfolio optimization system by using Neuro-Fuzzy framework in order to manage stock portfolio. It is great importance to stock investors and applied researchers. The proposed portfolio optimization approach Neuro-Fuzzy System reasoning in order to make a more yields from the stock portfolio, and hence maximize return and minimize risk of a stock portfolio through divers...

Recent advances in quantum technologies pave the way for noisy intermediate-scale (NISQ) devices, where approximation optimization algorithm (QAOA) constitutes a promising candidate demonstrating tangible advantages based on NISQ devices. In this paper, we consider maximum likelihood (ML) detection problem of binary symbols transmitted over multiple-input and multiple-output (MIMO) channel, fin...

An uncertainty set is a crucial component in robust optimization. Unfortunately, it is often unclear how to specify it precisely. Thus it is important to study sensitivity of the robust solution to variations in the uncertainty set, and to develop a method which improves stability of the robust solution. In this paper, to address these issues, we focus on uncertainty in the price impact paramet...

In optimization problems appearing in fields such as economics, finance, or engineering, it is often important that a risk measure of a decision-dependent random variable stays below a prescribed level. At the same time, the underlying probability distribution determining the risk measure’s value is typically known only up to a certain degree and the constraint should hold for a reasonably wide...

In this paper we present an algorithm for maximum likelihood estimation in the proportional odds model. The algorithm is an example of optimization transfer, also known as the method of iterative majorization. We discuss optimization transfer, present the proportional odds algorithm, and give a means for accelerating the convergence of the algorithm. The algorithm is stable and guaranteed to co...

The optimization of batch processes has received attention recently because, in the face of growing competition, it represents a natural choice for reducing production costs, improving product quality, meeting safety requirements or environmental regulations. The main bottleneck in using optimization in industry is the way uncertainty is handled, which forms the subject of this series of two pa...