Parallel skeletons for divide-and-conquer and branch-and-bound techniques

2003 ◽  
Author(s):  
I. Dorta ◽  
C. Leon ◽  
C. Rodriguez ◽  
A. Rojas
Keyword(s):  
Branch And Bound ◽  
Divide And Conquer ◽  
Parallel Skeletons ◽  
Branch And Bound Techniques

RANDOM SEEKING: A GENERAL, EFFICIENT, AND INFORMED RANDOMIZED SCHEME FOR DYNAMIC LOAD BALANCING

2000 ◽  
Vol 11 (02) ◽  
pp. 231-246 ◽  
Author(s):  
NIHAR R. MAHAPATRA ◽  
SHANTANU DUTT
Keyword(s):  
Artificial Intelligence ◽  
Load Balancing ◽  
Branch And Bound ◽  
Operations Research ◽  
Dynamic Load ◽  
Load Distribution ◽  
Search Algorithms ◽  
Dynamic Load Balancing ◽  
Divide And Conquer ◽  
Branch And Bound Algorithms

We propose a completely general, informed randomized dynamic load balancing method called random seeking (RS) suitable for parallel algorithms with characteristics found in many search algorithms used in artificial intelligence and operations research and many divide-and-conquer algorithms. In it, source processors randomly seek out sink processors for load balancing by flinging "probe" messages. These probes not only locate sinks, but also collect load distribution information which is used to efficiently regulate load balancing activities. We empirically compare RS with a well-known randomized dynamic load balancing method, the random communication (RC) strategy, by using them in parallel best-first branch-and-bound algorithms on up to 512 processors of a parallel system. We find that the RC execution times are more than those of RS by 16–67% when used to perform combined dynamic quantitative and qualitative load balancing, and by 9–74% when used to perform only dynamic quantitative load balancing.

GLOBAL OPTIMIZATION USING THE BRANCH‐AND‐BOUND ALGORITHM WITH A COMBINATION OF LIPSCHITZ BOUNDS OVER SIMPLICES

2009 ◽  
Vol 15 (2) ◽  
pp. 310-325 ◽  
Author(s):  
Remigijus Paulavičius ◽  
Julius Žilinskas
Keyword(s):  
Global Optimization ◽  
Branch And Bound ◽  
Optimization Problems ◽  
The Other ◽  
Branch And Bound Algorithm ◽  
Global Optimization Algorithm ◽  
Lipschitz Optimization ◽  
Branch And Bound Algorithms ◽  
Simple Bounds ◽  
Branch And Bound Techniques

Many problems in economy may be formulated as global optimization problems. Most numerically promising methods for solution of multivariate unconstrained Lipschitz optimization problems of dimension greater than 2 use rectangular or simplicial branch‐and‐bound techniques with computationally cheap, but rather crude lower bounds. The proposed branch‐and‐bound algorithm with simplicial partitions for global optimization uses a combination of 2 types of Lipschitz bounds. One is an improved Lipschitz bound with the first norm. The other is a combination of simple bounds with different norms. The efficiency of the proposed global optimization algorithm is evaluated experimentally and compared with the results of other well‐known algorithms. The proposed algorithm often outperforms the comparable branch‐and‐bound algorithms. Santrauka Daug įvairių ekonomikos uždavinių yra formuluojami kaip globaliojo optimizavimo uždaviniai. Didžioji dalis Lipšico globaliojo optimizavimo metodų, tinkamų spręsti didesnės dimensijos, t. y. n > 2, uždavinius, naudoja stačiakampį arba simpleksinį šakų ir rėžių metodus bei paprastesnius rėžius. Šiame darbe pasiūlytas simpleksinis šakų ir rėžių algoritmas, naudojantis dviejų tipų viršutinių rėžių junginį. Pirmasis yra pagerintas rėžis su pirmąja norma, kitas – trijų paprastesnių rėžių su skirtingomis normomis junginys. Gautieji eksperimentiniai pasiūlyto algoritmo rezultatai yra palyginti su kitų gerai žinomų Lipšico optimizavimo algoritmų rezultatais.

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