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- Francisco Facchinei

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
39
Citations
13,268
113
World Ranking
1430
National Ranking
45

Computer Science
D-index
39
Citations
13,060
112
World Ranking
5926
National Ranking
145

- Mathematical optimization
- Mathematical analysis
- Algorithm

His primary areas of investigation include Mathematical optimization, Variational inequality, Mathematical economics, Mixed complementarity problem and Complementarity theory. His work deals with themes such as Distributed algorithm, Convergence and Convex function, which intersect with Mathematical optimization. His work is dedicated to discovering how Distributed algorithm, Game theory are connected with Iterative method and Cognitive radio and other disciplines.

The study incorporates disciplines such as Uniqueness and Constrained optimization in addition to Variational inequality. In general Mixed complementarity problem study, his work on Nonlinear complementarity problem often relates to the realm of Numerical analysis, thereby connecting several areas of interest. His research combines Linear complementarity problem and Nonlinear complementarity problem.

- Finite-Dimensional Variational Inequalities and Complementarity Problems (3154 citations)
- Generalized Nash Equilibrium Problems (399 citations)
- A semismooth equation approach to the solution of nonlinear complementarity problems (325 citations)

His scientific interests lie mostly in Mathematical optimization, Convergence, Variational inequality, Constrained optimization and Nash equilibrium. His Mathematical optimization study combines topics from a wide range of disciplines, such as Distributed algorithm, Rate of convergence, Function and Algorithm. His work carried out in the field of Convergence brings together such families of science as Generalized nash equilibrium, Separable space, Optimization problem and Numerical analysis.

His work in Variational inequality addresses issues such as Karush–Kuhn–Tucker conditions, which are connected to fields such as Interior point method. His Constrained optimization research focuses on Penalty method and how it relates to Nonlinear programming, Lipschitz continuity, Applied mathematics, Class and Conjugate gradient method. His studies in Nash equilibrium integrate themes in fields like Game theory and Equilibrium selection.

- Mathematical optimization (69.35%)
- Convergence (23.39%)
- Variational inequality (18.55%)

- Mathematical optimization (69.35%)
- Convergence (23.39%)
- Function (11.29%)

Francisco Facchinei mainly investigates Mathematical optimization, Convergence, Function, Asynchronous communication and Convergence of random variables. His Mathematical optimization research includes themes of Differentiable function, Separable space and Convex function. His Convergence research incorporates themes from Optimization problem and Theoretical computer science.

Variational inequality, Convex set, Strongly monotone, Tikhonov regularization and Solution set is closely connected to Distributed algorithm in his research, which is encompassed under the umbrella topic of Optimization problem. His Variational inequality research is under the purview of Mathematical analysis. His Asynchronous communication research includes elements of Resource allocation, Statistical model, Speedup and Convex optimization.

- Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems (223 citations)
- Parallel and Distributed Methods for Constrained Nonconvex Optimization—Part I: Theory (145 citations)
- Parallel Selective Algorithms for Nonconvex Big Data Optimization (142 citations)

- Mathematical optimization
- Mathematical analysis
- Algorithm

Francisco Facchinei focuses on Mathematical optimization, Convergence, Optimization problem, Convex function and Function. His Minification and Variational inequality investigations are all subjects of Mathematical optimization research. His studies deal with areas such as Numerical analysis and Karush–Kuhn–Tucker conditions as well as Variational inequality.

The Optimization problem study combines topics in areas such as Sequence, Parallelizable manifold and Decomposition. His Convex function study combines topics in areas such as Multi-agent system and Heuristics. The various areas that Francisco Facchinei examines in his Function study include Machine learning, Convergence of random variables, Jacobi method, Algorithm and Differentiable function.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Finite-Dimensional Variational Inequalities and Complementarity Problems

Francisco Facchinei;Jong-Shi Pang.

**(2013)**

5121 Citations

Generalized Nash equilibrium problems

Francisco Facchinei;Christian Kanzow.

Annals of Operations Research **(2010)**

993 Citations

A semismooth equation approach to the solution of nonlinear complementarity problems

Tecla De Luca;Francisco Facchinei;Christian Kanzow.

Mathematical Programming **(1996)**

489 Citations

A smoothing method for mathematical programs with equilibrium constraints

Francisco Facchinei;Houyuan Jiang;Liqun Qi.

Mathematical Programming **(1999)**

415 Citations

A New Merit Function For Nonlinear Complementarity Problems And A Related Algorithm

Francisco Facchinei;João Soares.

Siam Journal on Optimization **(1997)**

344 Citations

Convex Optimization, Game Theory, and Variational Inequality Theory

Gesualdo Scutari;Daniel Palomar;Francisco Facchinei;Jong-shi Pang.

IEEE Signal Processing Magazine **(2010)**

336 Citations

On generalized Nash games and variational inequalities

Francisco Facchinei;Andreas Fischer;Veronica Piccialli.

Operations Research Letters **(2007)**

304 Citations

Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems

Gesualdo Scutari;Francisco Facchinei;Peiran Song;Daniel P. Palomar.

IEEE Transactions on Signal Processing **(2014)**

272 Citations

On the Accurate Identification of Active Constraints

Francisco Facchinei;Andreas Fischer;Christian Kanzow.

Siam Journal on Optimization **(1998)**

256 Citations

Design of Cognitive Radio Systems Under Temperature-Interference Constraints: A Variational Inequality Approach

Jong-Shi Pang;Gesualdo Scutari;Daniel P Palomar;Francisco Facchinei.

IEEE Transactions on Signal Processing **(2010)**

252 Citations

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