The workshop aims at presenting the state-of-the-art and current research on variational inequalities, Nash equilibrium problems and applications.

Recently, there has been a sharp increase in interest in variational inequalities. They are now one of the most challenging and dynamic topics of mathematics, and represent excellent tools in the study of real-world problems. In fact, they cover a large variety of applications of extreme importance related to computer science, mathematical physics, engineering, statistics, economics, financial networks and generalized complementarity problems.

In addition, Nash equilibrium problems, naturally associated with variational inequalities, experienced a surprising development. This leads to finding effective solutions to until now unsolved problems in numerous real-life situations, such as oligopoly models, environmental problems, network problems and infrastructure problems.

Variational methods and game theory are also used in computer vision, machine learning and pattern recognition, in order to obtain intelligent systems able to reconstruct the geometric structure of a scene starting from images; to infer the motion of objects in videos; to make the semantic partitioning of pixels in an image, and make it possible to learn and infer visual data using Bayesian models based on graphs.

The aim of the workshop is to bring together scholars working on both theoretical and computational issues, present results having the potential of solving concrete problems, and thus try to fill the gap between theory and practice. 

Papers are solicited in, but not limited to, the following TOPICS:

• Variational and quasi-variational inequalities
• Nash and generalized Nash equilibrium problems
• Equilibrium problems on networks both in the static and dynamic case
• Generalized complementarity problems
• Infinite dimensional duality theory
• Approximation and algorithms
• Variational methods in computer vision
• Variational methods in machine learning and pattern recognition
• Variational methods in artificial intelligence
• Game theory in computer vision
• Game theory in machine learning and pattern recognition
• Game theory in artifical intelligence
• Applications to computer science, engineering, management science, transportation, economics, environmental problems, biology, telecommunications networks, etc.