QUBO formulations of three NP problems

2021 ◽  
Vol 42 (7) ◽  
pp. 1625-1648
Author(s):  
Anuradha Mahasinghe ◽  
Vishmi Fernando ◽  
Paduma Samarawickrama
Keyword(s):  
Np Problems

scholarly journals No NP problems averaging over ranking of distributions are harder

1997 ◽  
Vol 181 (2) ◽  
pp. 229-245 ◽  
Author(s):  
Jay Belanger ◽  
Wang Jie
Keyword(s):  
Np Problems

Logical Description of Monotone NP Problems

1994 ◽  
Vol 4 (4) ◽  
pp. 337-357 ◽  
Author(s):  
IAIN A. STEWART
Keyword(s):  
Logical Description ◽  
Np Problems

A Novel Bio Inspired Algorithm Based on Echolocation Mechanism of Bats for Seismic States Prediction

2017 ◽  
Vol 8 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Mohamed Elhadi Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Data Mining ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Geographic Area ◽  
Seismic Hazards ◽  
Data Discovery ◽  
Distance Calculation ◽  
Hard Problems ◽  
Recorded Data ◽  
Np Problems

Bio-inspired algorithms are sort of implementation of natural solutions to solve hard problems – so called NP problems. A seismic hazard is the probability that an earthquake will occur in a given geographic area, within a given window of time, and with ground motion intensity exceeding a given threshold. Seismic hazards prediction is one of the fields where data mining plays an important role. This paper presents a new bio-inspired algorithm motivated by the echolocation behavior of bats for seismic hazard states prediction in coal mines based on previously recorded data. It is a distance calculation based approach, Results were very satisfactory in a manner that encourage us to continue working on this approach. The implementation of the algorithm touches three fields of studies, data discovery or so called data mining, bio inspired techniques, and seismic hazards predictions.

scholarly journals Location of Urban Logistic Terminals as Hub Location Problem

1970 ◽  
Vol 24 (5) ◽  
pp. 433-440 ◽  
Author(s):  
Jasmina Pašagić Škrinjar ◽  
Kristijan Rogić ◽  
Ratko Stanković
Keyword(s):  
Location Problem ◽  
Location Problems ◽  
Heuristic Methods ◽  
Hub Location ◽  
Hub Location Problem ◽  
Telecommunication Systems ◽  
Big Cities ◽  
Np Problems ◽  
Delivery Networks ◽  
Single Allocation

In this paper the problems of locating urban logistic terminals are studied as hub location problems that due to a large number of potential nodes in big cities belong to hard non-polynomial problems, the so-called NP-problems. The hub location problems have found wide application in physical planning of transport and telecommunication systems, especially systems of fast delivery, networks of logistic and distribution centres and cargo traffic terminals of the big cities, etc. The paper defines single and multiple allocations and studies the numerical examples. The capacitated single allocation hub location problems have been studied, with the provision of a mathematical model of selecting the location for the hubs on the network. The paper also presents the differences in the possibilities of implementing the exact and heuristic methods to solve the actual location problems of big dimensions i.e. hub problems of the big cities.

scholarly journals Decomposition and gluing for adiabatic quantum optimization

2014 ◽  
Vol 14 (11&12) ◽  
pp. 949-965
Author(s):  
Micah Blake McCurdy ◽  
Jeffrey Egger ◽  
Jordan Kyriakidis
Keyword(s):  
Numerical Results ◽  
Quantum Computers ◽  
Integer Factorization ◽  
Foreseeable Future ◽  
Decomposition Techniques ◽  
Np Problems ◽  
Quantum Optimization

Farhi and others~\cite{Farhi} have introduced the notion of solving NP problems using adiabatic quantum computers. We discuss an application of this idea to the problem of integer factorization, together with a technique we call \emph{gluing} which can be used to build adiabatic models of interesting problems. Although adiabatic quantum computers already exist, they are likely to be too small to directly tackle problems of interesting practical sizes for the foreseeable future. Therefore, we discuss techniques for decomposition of large problems, which permits us to fully exploit such hardware as may be available. Numerical results suggest that even simple decomposition techniques may yield acceptable results with subexponential overhead, independent of the performance of the underlying device.

Dealing with Hardness

2017 ◽  
Author(s):  
Lance Fortnow
Keyword(s):  
Brute Force ◽  
Complete Problem ◽  
Hard Problems ◽  
Np Complete ◽  
Np Problems ◽  
Single Technique

This chapter demonstrates several approaches for dealing with hard problems. These approaches include brute force, heuristics, and approximation. Typically, no single technique will suffice to handle the difficult NP problems one needs to solve. For moderate-sized problems one can search over all possible solutions with the very fast computers available today. One can use algorithms that might not work for every problem but do work for many of the problems one cares about. Other algorithms may not find the best possible solution but still a solution that's good enough. Other times one just cannot get a solution for an NP-complete problem. One has to try to solve a different problem or just give up.

Evolutionary Multi-Objective Optimization for DNA Sequence Design

2011 ◽  
pp. 239-264
Author(s):  
Soo-Yong Shin ◽  
In-Hee Lee ◽  
Byoung-Tak Zhang
Keyword(s):  
Evolutionary Algorithms ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Dna Microarrays ◽  
Superior Performance ◽  
Final Decision ◽  
Sequence Design ◽  
Multi Objective ◽  
Dna Sequence Design ◽  
Np Problems

Finding reliable and efficient DNA sequences is one of the most important tasks for successful DNArelated experiments such as DNA computing, DNA nano-assembly, DNA microarrays and polymerase chain reaction. Sequence design involves a number of heterogeneous and conflicting design criteria. Also, it is proven as a class of NP problems. These suggest that multi-objective evolutionary algorithms (MOEAs) are actually good candidates for DNA sequence optimization. In addition, the characteristics of MOEAs including simple addition/deletion of objectives and easy incorporation of various existing tools and human knowledge into the final decision process could increase the reliability of final DNA sequence set. In this chapter, we review multi-objective evolutionary approaches to DNA sequence design. In particular, we analyze the performance of e-multi-objective evolutionary algorithms on three DNA sequence design problems and validate the results by showing superior performance to previous techniques.

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