Based on the Lyapunov functional stability analysis for differential equations and the linear matrix inequality (LMI) optimization approach, A novel criterion for the global asymptotic stability of cellular neural networks with time-varying discrete and distributed delays is derived to guarantee global asymptotic stability. The criterion is expressed in terms of LMIs, which can be solved easily...

This paper concerns a method of selecting a subset of features for a logistic regression model. Information criteria, such as the Akaike information criterion and Bayesian information criterion, are employed as a goodness-offit measure. The feature subset selection problem is formulated as a mixed integer linear optimization problem, which can be solved with standard mathematical optimization s...

PURPOSE To describe and mathematically validate the superiorization methodology, which is a recently developed heuristic approach to optimization, and to discuss its applicability to medical physics problem formulations that specify the desired solution (of physically given or otherwise obtained constraints) by an optimization criterion. METHODS The superiorization methodology is presented as...

We take a single-level quantum dot embedded between two metallic leads at different temperatures and chemical potentials which works as a heat engine. Two optimization criteria were used and their corresponding optimized efficiencies, powers, and periods evaluated. A comparison between similar quantities of the two optimization criteria reveals mixed advantages and disadvantages. We quantify th...

The Pareto Optimal Model Assessment Cycle (POMAC), a multiple-criteria model assessment methodology, is described for exploring uncertainty in the relationships between ecological theory, model structure, and assessment data. Model performance is optimized to satisfy, simultaneously, each component of a vector of assessment criteria (model outputs), rather than the usual procedure of optimizing...

1. Abstract Complex and computationally intensive modeling and simulation of real-world engineering systems can include a large number of design variables in the optimization of such systems. Consequently, it is desirable to conduct variable screening to identify significant or active variables so that a simpler, more efficient, and accurate optimization process can be achieved. This paper empl...

Kelly's Criterion is well known among gamblers and investors as a method for maximizing the returns one would expect to observe over long periods of betting or investing. These ideas are conspicuously absent from portfolio optimization problems in the financial and automation literature. This paper will show how Kelly's Criterion can be incorporated into standard portfolio optimization models. ...

In this paper, we propose a novel approach to dynamic inverse optimization by the learning of neural networks. A dynamic inverse optimization problem here means to estimate a criterion function under which given input and output sequences become optimal for a known state equation model. A neural network architecture representing the optimality condition including an algebraic Riccati equation i...