'Function optimization' presentation slideshows

Boolean + Ranking: Querying a Database by K-Constrained Optimization

Boolean + Ranking: Querying a Database by K-Constrained Optimization. Zhen Zhang Seung-won Hwang Kevin C. Chang Min Wang Christian A. Lang Yuan-chi Chang Presented ACM SIGMOD Conference (SIGMOD 2006), Chicago, June 2006. Presented By : Pavan Kumar M.K. (1000618890)

552 views • 45 slides

The League Championship Algorithm: A new algorithm for numerical function optimization

The League Championship Algorithm: A new algorithm for numerical function optimization. By: A. H. Kashani. Introduction. Since the 1970s that the idea of a general algorithmic framework, which can be applied with relatively few modifications to different optimization problems , emerged.

441 views • 43 slides

Boolean + Ranking: Querying a Database by K-Constrained Optimization

Boolean + Ranking: Querying a Database by K-Constrained Optimization. Zhen Zhang Joint work with: Seung-won Hwang, Kevin C. Chang, Min Wang, Christian A. Lang, Yuan-chi Chang. Information retrieval. Traditional databases. Ranking query: Top 5 ranked by gpa. Boolean query:

372 views • 30 slides

CENG 562 MACHINE LEARNING

336 views • 21 slides

CENG 562 MACHINE LEARNING

216 views • 21 slides

Tutorial 2 (Lecture6)

229 views • 12 slides

Computer Aided Mechanical Design

Computer Aided Mechanical Design. History at Rice Tasks Reports Tools. ME CAD Tasks. Geometric Modelling Finite Element Analysis [FEA] Computational Fluid Dynamics [CFD] Drafting, Rapid Prototypes [STL] Parts, Assemblies, Constraints Mechanisms. ME CAD Tasks.

219 views • 11 slides

Particle Swarm Optimization

Particle Swarm Optimization. James Kennedy & Russel C. Eberhart. Idea Originator. Landing of Bird Flocks Function Optimization Thinking is Social Collisions are allowed. Simple Model. Swarm of Particles Position in Solution Space New Position by Random Steps

447 views • 11 slides

Function Optimization Vallary Bhopatkar Michael Phipps

Function Optimization Vallary Bhopatkar Michael Phipps. Two Techniques Function Evaluation (Sections 10.2, 10.3) Derivative Evaluation (Section 10.4). 1d Minimization. When finding roots: the root is bracketed if a pair of points, a and b, have opposite signs from the function

225 views • 10 slides

Population Based Incremental Learning

Population Based Incremental Learning. Shumeet Baluja Presented by KC Tsui. Background. Populations based search, such as GA Create a probability matrix by counting the number of 1s and 0s in each gene position Generate new population using the probability matrix

316 views • 8 slides