Portfolio optimization is one of the most important investment strategies in financial markets.
It is practically desirable for investors, especially high-frequency traders, to consider …

This paper addresses portfolio selection based on neurodynamic optimization. The portfolio
selection problem is formulated as a biconvex optimization problem with a variable weight in …

As two important types of generalized convex functions, pseudoconvex and quasiconvex
functions appear in many practical optimization problems. The lack of convexity poses some …

This paper presents a neurodynamic approach to nonlinear optimization problems with
affine equality and convex inequality constraints. The proposed neural network endows with …

Distributed optimization problem (DOP) over multi-agent systems, which can be described
by minimizing the sum of agents' local objective functions, has recently attracted widespread …

This paper addresses portfolio selection with targeted performance criteria via
neurodynamic optimization. Five portfolio optimization problems are formulated with a …

Feature selection is an important issue in machine learning and data mining. Most existing
feature selection methods are greedy in nature thus are prone to sub-optimality. Though …

This paper proposes a deep learning approach for solving non-smooth convex optimization
problems (NCOPs), which have broad applications in computer science, engineering, and …

Complex-variable pseudoconvex optimization has been widely used in numerous scientific
and engineering optimization problems. A neurodynamic approach is proposed in this paper …

This article proposes a novel adaptive neurodynamic algorithm (ANA) to seek generalized
Nash equilibrium (GNE) of the noncooperative constrained game with different monotone …