On the solution existence and stability of polynomial optimization problems

This paper introduces and investigates a regularity condition in the asymptotic sense for the optimization problems whose objective functions are polynomial. We prove two sufficient conditions for the existence of solutions for polynomial optimization problems. Further, when the constraint sets are semi-algebraic, we show results on the stability of the solution map of polynomial optimization problems. At the end of the paper, we discuss the genericity of the regularity condition.Comment: The old title: A regularity condition in polynomial optimizatio

Computing local minimizers in polynomial optimization under genericity conditions

In this paper, we aim at computing all local minimizers of a polynomial optimization problem under genericity conditions. By using a technique in computer algebra, we provide a univariate representation for the set of local minimizers. In particular, for an unconstrained problem, the coordinates of all local minimizers can be represented by several univariate polynomial equalities and one univariate polynomial matrix inequality. We also develop the technique for constrained problems having equality constraints. Based on the above technique, we design algorithms to enumerate the local minimizers. At the end of the paper, we provide some experimental examples.Comment: 24 pages, submitte

Performance Analysis of Hybrid ALOHA/CDMA RFID Systems with Quasi-decorrelating Detector in Noisy Channels

In this paper we investigate the performance of a hybrid Aloha/CDMA radio frequency identification (RFID) system with quasi-decorrelating detector (QDD). Motivated by the fact that the QDD outperforms the conventional decorrelating detector (DD) in noisy network scenarios, we study and propose using QDD as one of the most promising candidates for the structure of RFID readers. Performance analysis in terms of bit error rate and the RFID system efficiency is considered considering CDMA code collision and detection error. Computer simulations are also performed, and the obtained results of QDD-based structure are compared with those of DD-based one to confirm the correctness of the design suggestion in different practical applications of tag identification and missing-tag detection

DEFEG: deep ensemble with weighted feature generation.

With the significant breakthrough of Deep Neural Networks in recent years, multi-layer architecture has influenced other sub-fields of machine learning including ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcForest that involves several layers of Random Forest-based classifiers. Although gcForest has outperformed several benchmark algorithms on specific datasets in terms of classification accuracy and model complexity, its input features do not ensure better performance when going deeply through layer-by-layer architecture. We address this limitation by introducing a deep ensemble model with a novel feature generation module. Unlike gcForest where the original features are concatenated to the outputs of classifiers to generate the input features for the subsequent layer, we integrate weights on the classifiers’ outputs as augmented features to grow the deep model. The usage of weights in the feature generation process can adjust the input data of each layer, leading the better results for the deep model. We encode the weights using variable-length encoding and develop a variable-length Particle Swarm Optimisation method to search for the optimal values of the weights by maximizing the classification accuracy on the validation data. Experiments on a number of UCI datasets confirm the benefit of the proposed method compared to some well-known benchmark algorithms