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On the convergence of stochastic primal-dual hybrid gradient

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The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity

Bernard Dacorogna

Any finite, separately convex, positively homogeneous function on

\mathbb{R}^2

is convex. This was first established by the first author ["Direct methods in calculus of variations", Springer-Verlag (1989)]. Here we give a new and concise proof of this re ...

2008

On the convergence of stochastic primal-dual hybrid gradient

Graph Chatbot

Chattez avec Graph Search

Multiple Object Tracking using Flow Linear Programming

Multiple Object Tracking using Flow Linear Programming

The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity

Fixed-Order Controller Design for Systems with Polytopic Uncertainty Using LMIs

Robust Pole Placement of Systems with Polytopic Uncertainty Via Convex Optimization

Energy-aware stage illumination

Perfect Simulation and Stationarity of a Class of Mobility Models

Perfect Simulation and Stationarity of a Class of Mobility Models

Finding minimum area simple pentagons

Perfect Simulation and Stationarity of a Class of Mobility Models

Perfect Simulation and Stationarity of a Class of Mobility Models

Fixed-Order Controller Design for Systems with Polytopic Uncertainty Using LMIs

Robust Pole Placement of Systems with Polytopic Uncertainty Via Convex Optimization

Multiple Object Tracking using Flow Linear Programming

Finding minimum area simple pentagons

Multiple Object Tracking using Flow Linear Programming

The role of perspective functions in convexity, polyconvexity, rank-one convexity and separate convexity

Energy-aware stage illumination