Abstract When solving large-scale cardinality-constrained Markowitz mean–variance portfolio investment problems, exact solvers may be unable to derive some efficient portfolios, even within a reasonable time limit. In such cases, information on the distance from best feasible solution, found before optimization process has stopped, true solution is unavailable. this article, I demonstrate how p...

CAN THE NORMALITY OF THE SEMI VARIANCE BE IMPROVED? EVIDENCE FROM FINANCIAL STOCK INDEXES WITH HOURLY, DAILY, QUARTERLY AND ANNUAL DATA OF DJIA AND SP500 ELDOMIATY, Tarek Ibrahim Abstract This study examines the financial and statistical properties of the variance and semi variance (SV). Since the mean-variance approach and its extended mean-semi variance approach assume normality of returns, i...

We provide a new characterization of mean-variance hedging strategies in a general semimartingale market. The key point is the introduction of a new probability measure P ⋆ which turns the dynamic asset allocation problem into a myopic one. The minimal martingale measure relative to P ⋆ coincides with the variance-optimal martin-gale measure relative to the original probability measure P .

We present a threshold-based cardinality inimization formulation for the security-constrained economic dispatch problem. The model aims to minimize operating cost of system while simultaneously reducing number lines in emergency zones during contingency events. allows operator monitor duration which operate and ensure that they are within acceptable reliability standards determined by operators...

This paper is concerned with the variance-constrained state estimation problem for a class of networked multi-rate systems (NMSs) with network-induced probabilistic sensor failures and measurement quantization. The stochastic characteristics of the sensor failures are governed by mutually independent random variables over the interval [0, 1]. By applying the lifting technique, an augmented syst...

Sparse PCA seeks approximate sparse “eigenvectors” whose projections capture the maximal variance of data. As a cardinality-constrained and non-convex optimization problem, it is NP-hard and is encountered in a wide range of applied fields, from bio-informatics to finance. Recent progress has focused mainly on continuous approximation and convex relaxation of the hard cardinality constraint. In...