Cooperative Optimization for Energy Minimization: A Case Study of Stereo Matching
| dc.creator | Huang, Xiaofei | |
| dc.date | 2007-01-09 | |
| dc.date.accessioned | 2026-07-07T07:39:22Z | |
| dc.date.available | 2026-07-07T07:39:22Z | |
| dc.description | Often times, individuals working together as a team can solve hard problems beyond the capability of any individual in the team. Cooperative optimization is a newly proposed general method for attacking hard optimization problems inspired by cooperation principles in team playing. It has an established theoretical foundation and has demonstrated outstanding performances in solving real-world optimization problems. With some general settings, a cooperative optimization algorithm has a unique equilibrium and converges to it with an exponential rate regardless initial conditions and insensitive to perturbations. It also possesses a number of global optimality conditions for identifying global optima so that it can terminate its search process efficiently. This paper offers a general description of cooperative optimization, addresses a number of design issues, and presents a case study to demonstrate its power. | |
| dc.identifier | https://arxiv.org/abs/cs/0701057 | |
| dc.identifier | http://arxiv.org/abs/cs/0701057 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/121421 | |
| dc.subject | Computer Vision and Pattern Recognition | |
| dc.subject | Artificial Intelligence | |
| dc.title | Cooperative Optimization for Energy Minimization: A Case Study of Stereo Matching | |
| dc.type | text |