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.
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.
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.
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.
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Finite-Dimensional Variational Inequalities and Complementarity Problems
Francisco Facchinei;Jong-Shi Pang.
Generalized Nash equilibrium problems
Francisco Facchinei;Christian Kanzow.
Annals of Operations Research (2010)
A semismooth equation approach to the solution of nonlinear complementarity problems
Tecla De Luca;Francisco Facchinei;Christian Kanzow.
Mathematical Programming (1996)
A smoothing method for mathematical programs with equilibrium constraints
Francisco Facchinei;Houyuan Jiang;Liqun Qi.
Mathematical Programming (1999)
A New Merit Function For Nonlinear Complementarity Problems And A Related Algorithm
Francisco Facchinei;João Soares.
Siam Journal on Optimization (1997)
Convex Optimization, Game Theory, and Variational Inequality Theory
Gesualdo Scutari;Daniel Palomar;Francisco Facchinei;Jong-shi Pang.
IEEE Signal Processing Magazine (2010)
On generalized Nash games and variational inequalities
Francisco Facchinei;Andreas Fischer;Veronica Piccialli.
Operations Research Letters (2007)
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)
On the Accurate Identification of Active Constraints
Francisco Facchinei;Andreas Fischer;Christian Kanzow.
Siam Journal on Optimization (1998)
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)
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