A new modified BFGS method for solving systems of nonlinear equations

Dehghani, Razieh; Bidabadi, Narges; Hosseini, Mohammad Mehdi

doi:10.1080/09720502.2019.1574065

Cited by 11 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Electrons will flow from the lower work function to the higher work function when two substances with different work functions contact each other, resulting in different electron concentrations and potentials on both sides. [ 12 ] In this study, the work function ɸ of P(VDF‐TrFE) (Figure S1, Supporting Information), BTNF and BTNP (the insets in Figure 1b) were 7.274, 2.942, and 3.189 eV, respectively. This clearly shows that the work function of BTNF is lower than that of BTNP, after the formation of heterojunction between P(VDF‐TrFE) and BTO nanofibers or BTO nanoparticles, indicating that the electron generating capacity of BTNF is lower than that of BTNP.…”

Section: Resultsmentioning

confidence: 61%

Manipulation of Heterogeneous Surface Electric Potential Promotes Osteogenesis by Strengthening RGD Peptide Binding and Cellular Mechanosensing

et al. 2023

View full text Add to dashboard Cite

The heterogeneity of extracellular matrix (ECM) topology, stiffness, and architecture is a key factor modulating cellular behavior and osteogenesis. However, the effects of heterogeneous ECM electric potential at the micro‐ and nanoscale on osteogenesis remain to be elucidated. Here, the heterogeneous distribution of surface potential is established by incorporating ferroelectric BaTiO3 nanofibers (BTNF) into poly(vinylidene fluoridetrifluoroethylene) (P(VDF‐TrFE)) matrix based on phase‐field and first‐principles simulation. By optimizing the aspect ratios of BTNF fillers, the anisotropic distribution of surface potential on BTNF/P(VDF‐TrFE) nanocomposite membranes can be achieved by strong spontaneous electric polarization of BTNF fillers. These results indicate that heterogeneous surface potential distribution leads to a meshwork pattern of fibronectin (FN) aggregation, which increased FN‐III7‐10 (FN fragment) focal flexibility and anchor points as predicted by molecular dynamics simulation. Furthermore, integrin clustering, focal adhesion formation, cell spreading, and adhesion are enhanced sequentially. Increased traction of actin fibers amplifies mechanotransduction by promoting nuclear translocation of YAP/Runx2, which enhances osteogenesis in vitro and bone regeneration in vivo. The work thus provides fundamental insights into the biological effects of surface potential heterogeneity at the micro‐ and nanoscale on osteogenesis, and also develops a new strategy to optimize the performance of electroactive biomaterials for tissue regenerative therapies.

show abstract

Section: Resultsmentioning

confidence: 61%

Manipulation of Heterogeneous Surface Electric Potential Promotes Osteogenesis by Strengthening RGD Peptide Binding and Cellular Mechanosensing

et al. 2023

View full text Add to dashboard Cite

show abstract

“…As known, BFGS and DFP methods are regarded as the most popular and efficient QN methods. Recently, the methods have been much heeded in practical applications such as image processing [37,48], time series prediction [49], neural networks training [17,27], document categorization [21], managing demands in the water distribution networks [53], machine learning [4], robotics [50], solving systems of nonlinear equations [3,23,55], curve fitting by B-splines [26], matrix approximation in Frobenius norm [42], computing the matrix geometric mean [52] and estimating unitary symmetric eigenvalues of the complex tensors [18]. The methods have been also well-combined with the classical optimization tools such as conjugate gradient methods [8,16,22,35,36] as well as the metaheuristic algorithms [17,38,48].…”

mentioning

confidence: 99%

Diagonally scaled memoryless quasi–Newton methods with application to compressed sensing

Aminifard

Babaie-Kafaki

2023

JIMO

View full text Add to dashboard Cite

<p style='text-indent:20px;'>Memoryless quasi–Newton updating formulas of BFGS (Broyden–Fletcher–Goldfarb–Shanno) and DFP (Davidon–Fletcher–Powell) are scaled using well-structured diagonal matrices. In the scaling approach, diagonal elements as well as eigenvalues of the scaled memoryless quasi–Newton updating formulas play significant roles. Convergence analysis of the given diagonally scaled quasi–Newton methods is discussed. At last, performance of the methods is numerically tested on a set of CUTEr problems as well as the compressed sensing problem.</p>

show abstract

Nonmonotone diagonally scaled limited-memory BFGS methods with application to compressive sensing based on a penalty model

Babaie-Kafaki

Aminifard

Ghafoori

2022

Applied Numerical Mathematics

View full text Add to dashboard Cite

A new modified BFGS method for solving systems of nonlinear equations

Cited by 11 publications

References 13 publications

Manipulation of Heterogeneous Surface Electric Potential Promotes Osteogenesis by Strengthening RGD Peptide Binding and Cellular Mechanosensing

Manipulation of Heterogeneous Surface Electric Potential Promotes Osteogenesis by Strengthening RGD Peptide Binding and Cellular Mechanosensing

Diagonally scaled memoryless quasi–Newton methods with application to compressed sensing

Nonmonotone diagonally scaled limited-memory BFGS methods with application to compressive sensing based on a penalty model

Contact Info

Product

Resources

About