Non-negative matrix factorization (NMF) is a problem to obtain a representation of data using non-negativity constraints. Since the NMF was first proposed by Lee, NMF has attracted much attention for over a decade and has been successfully applied to numerous data analysis problems. Recent...

The convenience and rapidity of financial leasing modes in the peer-to-peer (P2P) platform enable small and medium-sized enterprises (SMEs) to solve financing problems. The core of risk management in the P2P platform is to improve the quality of the docking assets. Therefore, the purpose of this...

The quadratically convergent algorithms for training SVM with smoothing methods are discussed in this paper. By smoothing the objective function of an SVM formulation, Lee and Mangasarian [Comput. Optim. Appl. 20(1):5-22, <CitationRef CitationID="CR17">2001</CitationRef>] presented one such algorithm called SSVM and proved that the error...</citationref>

In this paper, we propose a second-order corrector interior-point algorithm for semidefinite programming (SDP). This algorithm is based on the wide neighborhood. The complexity bound is <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$${O(\sqrt{n}L)}$$</EquationSource> </InlineEquation> for the Nesterov-Todd direction, which coincides with the best known complexity results...</equationsource></inlineequation>

In this paper, we propose a second-order corrector interior-point algorithm for semidefinite programming (SDP). This algorithm is based on the wide neighborhood. The complexity bound is $${O(\sqrt{n}L)}$$ for the Nesterov-Todd direction, which coincides with the best known complexity results for...

In this paper, we present a meta-heuristic base on improved shuffled frog leaping algorithm (SFLA) to tackle the multi-objective problem (MOP). The SFLA is suitable to solve the single objective problem. For the multi-objective problem, one main issue is that how to evaluate the quality of two...