Bingsheng He

What is he best known for?

The fields of study he is best known for:

• Mathematical analysis
• Algebra
• Mathematical optimization

His primary areas of study are Mathematical optimization, Variational inequality, Applied mathematics, Numerical analysis and Mathematical analysis. His studies in Mathematical optimization integrate themes in fields like Algorithm, Sequence, Equilibrium problem and Solution set. His research in Variational inequality focuses on subjects like Theory of computation, which are connected to Proximal point method and Numerical tests.

His Applied mathematics study combines topics from a wide range of disciplines, such as Separable space, Magnitude and Convex optimization. His Convex optimization study combines topics in areas such as Rate of convergence, Convex function and Extension. His research investigates the connection between Mathematical analysis and topics such as Iterative method that intersect with issues in Convex set, Contraction, Monotonic function and Iterated function.

His most cited work include:

• On the $O(1/n)$ Convergence Rate of the Douglas-Rachford Alternating Direction Method (655 citations)
• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent (418 citations)
• A new inexact alternating directions method for monotone variational inequalities (326 citations)

What are the main themes of his work throughout his whole career to date?

Mathematical optimization, Variational inequality, Convex optimization, Applied mathematics and Rate of convergence are his primary areas of study. His work on Augmented Lagrangian method as part of general Mathematical optimization study is frequently linked to Contraction method, therefore connecting diverse disciplines of science. Variational inequality is a primary field of his research addressed under Mathematical analysis.

His work is dedicated to discovering how Convex optimization, Separable space are connected with Extension and other disciplines. His study in Applied mathematics is interdisciplinary in nature, drawing from both Lagrange multiplier, Sequence and Descent. His Rate of convergence research includes elements of Ergodic theory, Regularization, Simple and Gradient method.

He most often published in these fields:

• Mathematical optimization (63.64%)
• Variational inequality (57.95%)
• Convex optimization (57.95%)

What were the highlights of his more recent work (between 2013-2020)?

• Convex optimization (57.95%)
• Rate of convergence (35.23%)
• Mathematical optimization (63.64%)

In recent papers he was focusing on the following fields of study:

Bingsheng He mainly investigates Convex optimization, Rate of convergence, Mathematical optimization, Separable space and Applied mathematics. His work investigates the relationship between Convex optimization and topics such as Augmented Lagrangian method that intersect with problems in Jacobian matrix and determinant. His studies deal with areas such as Ergodic theory, Mathematical analysis, Simple and Function as well as Rate of convergence.

His Mathematical optimization study focuses on Variational inequality in particular. His research integrates issues of Saddle point, Gradient method and Contraction in his study of Variational inequality. His research in Applied mathematics tackles topics such as Lagrange multiplier which are related to areas like Relaxation factor.

Between 2013 and 2020, his most popular works were:

• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent (418 citations)
• On non-ergodic convergence rate of Douglas---Rachford alternating direction method of multipliers (198 citations)
• A STRICTLY CONTRACTIVE PEACEMAN-RACHFORD SPLITTING METHOD FOR CONVEX PROGRAMMING. (94 citations)

In his most recent research, the most cited papers focused on:

• Mathematical analysis
• Algebra
• Mathematical optimization

His main research concerns Mathematical optimization, Convex optimization, Rate of convergence, Applied mathematics and Separable space. His work on Variational inequality as part of his general Mathematical optimization study is frequently connected to Divergence, thereby bridging the divide between different branches of science. His Variational inequality study incorporates themes from Algorithm design, Lipschitz continuity and Contraction.

His Rate of convergence research is multidisciplinary, incorporating elements of Ergodic theory and Numerical analysis. His Applied mathematics research integrates issues from Convex combination, Subderivative, Convex analysis and Convex set. His research in Separable space intersects with topics in Solution set, Approximate solution, Jacobian matrix and determinant and Augmented Lagrangian method.