This paper addresses the problem of minimizing an energy function by means of a monotonic transformation. With an observation on global optimality of functions under such a transformation, we show that a simple and effective algorithm can be derived to search within possible regions containing the global optima. Numerical experiments are performed to compare this algorithm with one that does not incorporatetransformed information using several benchmark problems. These results are also compared to best known global search algorithms in the literature. In addition, the algorithm is shown to be useful for a class of neural network learning problems, which possess much larger parameter spaces.
|Title of host publication||Energy Minimization Methods in Computer Vision and Pattern Recognition - 3rd International Workshop, EMMCVPR 2001, Proceedings|
|Editors||Anil K. Jain, Mario Figueiredo, Josiane Zerubia|
|Number of pages||16|
|ISBN (Print)||3540425233, 9783540425236|
|Publication status||Published - 2001|
|Event||3rd International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2001 - Sophia Antipolis, France|
Duration: 2001 Sept 3 → 2001 Sept 5
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||3rd International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2001|
|Period||01/9/3 → 01/9/5|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)