On the performance guarantee of First Fit for sum coloring

2019 ◽  
Vol 99 ◽  
pp. 91-105
Author(s):  
Leah Epstein ◽  
Asaf Levin
Keyword(s):  
Performance Guarantee ◽  
Sum Coloring

scholarly journals Efficiency Analysis of Government Subsidy and Performance Guarantee Policies in Relation to PPP Infrastructure Projects

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Lei Shi ◽  
Yujia He ◽  
Masamitsu Onishi ◽  
Kiyoshi Kobayashi
Keyword(s):  
Government Subsidy ◽  
Performance Guarantee ◽  
Infrastructure Projects ◽  
Special Purpose Vehicle ◽  
Performance Guarantees ◽  
Sustainable Operation ◽  
Integrated Policy ◽  
The Government ◽  
And Performance ◽  
Liquidity Shock

Sustainable operation of public-private partnership (PPP) infrastructure projects that are characterized by considerable external benefits is of vital importance. However, a liquidity shock might trigger an inefficient liquidation of a project by the special purpose vehicle (SPV) and the bank, whose objectives are to maximize the profits generated by the project. This study argues that performance guarantee and subsidy policies implemented by the government play a role in encouraging socially efficient decision-making by the SPV and the bank to ensure the continuation of socially valuable projects. The results show that both government subsidy and performance guarantee policies are effective in avoiding the inefficient liquidation of PPP infrastructure projects when the external benefits are large and certain. However, a performance guarantee policy might lead to inefficient continuation when the external benefits of a project are uncertain. Finally, we discuss the possibility that an integrated policy combining performance guarantees and government subsidies improves the efficiency of a PPP infrastructure project.

scholarly journals Inapproximability and Polynomial-Time Approximation Algorithm for UET Tasks on Structured Processor Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
M. Bouznif ◽  
R. Giroudeau
Keyword(s):  
Approximation Algorithm ◽  
Large Class ◽  
Bipartite Graph ◽  
Polynomial Time ◽  
Performance Guarantee ◽  
Makespan Minimization ◽  
Time Approximation ◽  
Polynomial Time Approximation Algorithm ◽  
Precedence Graph ◽  
Polynomial Time Approximation

We investigate complexity and approximation results on a processor networks where the communication delay depends on the distance between the processors performing tasks. We then prove that there is no heuristic with a performance guarantee smaller than 4/3 for makespan minimization for precedence graph on a large class of processor networks like hypercube, grid, torus, and so forth, with a fixed diameter . We extend complexity results when the precedence graph is a bipartite graph. We also design an efficient polynomial-time -approximation algorithm for the makespan minimization on processor networks with diameter .

Improved RIP‐based performance guarantee for sparse signal recovery via A*OMP

2018 ◽  
Vol 54 (21) ◽  
pp. 1216-1218 ◽  
Author(s):  
Haifeng Li ◽  
Guoqi Liu ◽  
Jian Zou
Keyword(s):  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Performance Guarantee

Time series prediction with performance guarantee

2011 ◽  
Vol 5 (8) ◽  
pp. 1044-1051 ◽  
Author(s):  
M. Dashevskiy ◽  
Z. Luo
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Performance Guarantee

scholarly journals Asynchronous Delay-Aware Accelerated Proximal Coordinate Descent for Nonconvex Nonsmooth Problems

2019 ◽  
Vol 33 ◽  
pp. 1528-1535
Author(s):  
Ehsan Kazemi ◽  
Liqiang Wang
Keyword(s):  
Large Scale ◽  
Convergence Rates ◽  
Optimization Problems ◽  
Coordinate Descent ◽  
Performance Guarantee ◽  
Nonsmooth Problems ◽  
Descent Property ◽  
Large Scale Problems ◽  
Nonconvex And Nonsmooth Optimization ◽  
Sufficient Descent

Nonconvex and nonsmooth problems have recently attracted considerable attention in machine learning. However, developing efficient methods for the nonconvex and nonsmooth optimization problems with certain performance guarantee remains a challenge. Proximal coordinate descent (PCD) has been widely used for solving optimization problems, but the knowledge of PCD methods in the nonconvex setting is very limited. On the other hand, the asynchronous proximal coordinate descent (APCD) recently have received much attention in order to solve large-scale problems. However, the accelerated variants of APCD algorithms are rarely studied. In this paper, we extend APCD method to the accelerated algorithm (AAPCD) for nonsmooth and nonconvex problems that satisfies the sufficient descent property, by comparing between the function values at proximal update and a linear extrapolated point using a delay-aware momentum value. To the best of our knowledge, we are the first to provide stochastic and deterministic accelerated extension of APCD algorithms for general nonconvex and nonsmooth problems ensuring that for both bounded delays and unbounded delays every limit point is a critical point. By leveraging Kurdyka-Łojasiewicz property, we will show linear and sublinear convergence rates for the deterministic AAPCD with bounded delays. Numerical results demonstrate the practical efficiency of our algorithm in speed.

Sign in / Sign up

Export Citation Format

Share Document