VEAP: Global optimization based efficient algorithm for VLSI placement

2002 ◽  
Author(s):  
Kong Tianming ◽  
Hong Xianlong ◽  
Qiao Changge
Keyword(s):  
Global Optimization ◽  
Efficient Algorithm

An Efficient Algorithm for Global Optimization in Redundant Manipulations

1989 ◽  
Vol 111 (4) ◽  
pp. 488-493 ◽  
Author(s):  
Z. Wang ◽  
K. Kazerounian
Keyword(s):  
Global Optimization ◽  
Boundary Value Problem ◽  
Objective Function ◽  
Efficient Algorithm ◽  
Boundary Value ◽  
Robotic Manipulators ◽  
Exact Boundary ◽  
Task Period

A method is developed to resolve the redundancy of serial robotic manipulators. The main characteristics of this method are as follows: (a) The solution is conservative (unique); (b) It globally (over the task period) optimizes the objective function; (c) It is computationally very fast. In fact, it is about three orders of magnitude faster than solving for the exact boundary value problem.

Computational Micrograph Registration with Sieve Processes

1996 ◽  
Vol 54 ◽  
pp. 440-441
Author(s):  
P.J. Phillips ◽  
J. Huang ◽  
S. M. Dunn
Keyword(s):  
Planar Graph ◽  
Efficient Algorithm ◽  
Spatial Organization ◽  
Spatial Arrangement ◽  
Stereo Image ◽  
Image Model ◽  
Geometric Invariants ◽  
Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

Global Optimization

1993 ◽  
Author(s):  
Reiner Horst ◽  
Hoang Tuy
Keyword(s):  
Global Optimization

Probability Models in Global Optimization

Informatica ◽  
2016 ◽  
Vol 27 (2) ◽  
pp. 323-334 ◽  
Author(s):  
James M. Calvin
Keyword(s):  
Global Optimization ◽  
Probability Models
Sign in / Sign up

Export Citation Format

Share Document