(Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision

2016 ◽  
Vol 10 (1) ◽  
pp. 1-102 ◽  
Author(s):  
Nikos Komodakis ◽  
M. Pawan Kumar ◽  
Nikos Paragios
Keyword(s):  
Artificial Vision ◽  
Convex Relaxations

(Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision

2016 ◽  
Author(s):  
Nikos Komodakis ◽  
M. Pawan Kumar ◽  
Nikos Paragios
Keyword(s):  
Artificial Vision ◽  
Convex Relaxations

scholarly journals An alternative perspective on copositive and convex relaxations of nonconvex quadratic programs

2021 ◽  
Author(s):  
E. Alper Yıldırım
Keyword(s):  
Objective Function ◽  
Convex Relaxation ◽  
Large Family ◽  
Quadratic Program ◽  
Feasible Region ◽  
Recession Cone ◽  
Convex Relaxations ◽  
Quadratic Programs ◽  
Alternative Perspective ◽  
Optimal Value

AbstractWe study convex relaxations of nonconvex quadratic programs. We identify a family of so-called feasibility preserving convex relaxations, which includes the well-known copositive and doubly nonnegative relaxations, with the property that the convex relaxation is feasible if and only if the nonconvex quadratic program is feasible. We observe that each convex relaxation in this family implicitly induces a convex underestimator of the objective function on the feasible region of the quadratic program. This alternative perspective on convex relaxations enables us to establish several useful properties of the corresponding convex underestimators. In particular, if the recession cone of the feasible region of the quadratic program does not contain any directions of negative curvature, we show that the convex underestimator arising from the copositive relaxation is precisely the convex envelope of the objective function of the quadratic program, strengthening Burer’s well-known result on the exactness of the copositive relaxation in the case of nonconvex quadratic programs. We also present an algorithmic recipe for constructing instances of quadratic programs with a finite optimal value but an unbounded relaxation for a rather large family of convex relaxations including the doubly nonnegative relaxation.

scholarly journals Artificial vision

Eye ◽  
1998 ◽  
Vol 12 (3) ◽  
pp. 605-607 ◽  
Author(s):  
Mark S Humayun ◽  
Eugene de Juan
Keyword(s):  
Artificial Vision

Neural-like model of artificial vision for pattern analysis

1998 ◽  
Author(s):  
Alexander V. Pavlov
Keyword(s):  
Pattern Analysis ◽  
Artificial Vision

A Two-Channel Artificial Vision System for Motion Analysis

2003 ◽  
Vol 43 (9) ◽  
pp. 1271-1279
Author(s):  
Alexis Quesada-Arencibia ◽  
Roberto Moreno-Díaz ◽  
Miguel Aleman-Flores
Keyword(s):  
Motion Analysis ◽  
Vision System ◽  
Artificial Vision

Tampoprint inspection by artificial vision

2002 ◽  
Author(s):  
F. Truchetet ◽  
J.P. Cholley ◽  
S. Hemmings
Keyword(s):  
Artificial Vision

Generation of Emergent Navigation Behavior in Autonomous Agents Using Artificial Vision

2014 ◽  
Author(s):  
Lilian de O. Carneiro ◽  
Joaquim B. Cavalcante Neto ◽  
Creto A. Vidal ◽  
Yuri L.B. Nogueira ◽  
Arnaldo B. Vila Nova
Keyword(s):  
Autonomous Agents ◽  
Artificial Vision
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