Solving Systems of Difference Constraints Incrementally

Algorithmica ◽  
1999 ◽  
Vol 23 (3) ◽  
pp. 261-275 ◽  
Author(s):  
G. Ramalingam ◽  
J. Song ◽  
L. Joskowicz ◽  
R. E. Miller
Keyword(s):  
Difference Constraints

PENGARUH KUALITAS PELAYANAN DAN PROMOSI TERHADAP PERLUASAN PANGSA PASAR PRODUK DANA TALANGAN HAJI DI BANK SYARIAH MANDIRI KCP UJUNGBERUNG DAN KCP RANCAEKEK BANDUNG

2018 ◽  
Vol 2 (1) ◽  
pp. 1-15
Author(s):  
Intan Nurrachmi
Keyword(s):  
Quality Of Service ◽  
Service Quality ◽  
Market Share ◽  
Significant Influence ◽  
Strong Correlation ◽  
T Test ◽  
Market Expansion ◽  
Difference Constraints ◽  
Branch Office

This study departs from the hajj bailout financing facility which is a booming product because of the customer's interest, but in this case there is a difference in the target achievement between Bank Syariah Mandiri (BSM) Ujungberung KCP which is less successful in improving the hajj bailout products while the Rancaekek KCP is very superior in one consolidation Ahmad Yani Branch Office Bandung. This is what is interesting for researchers to carry out this research, the difference constraints include service quality and promotion factors. This phenomenon raises problems that must be examined, namely how the influence of service quality and promotion of market share expansion products hajj bailouts at Bank Syariah Mandiri KCP Ujungberung and KCP Rancaekek Bandung. This study aims academically to contribute in the study of Islamic economics in worksheets, especially the quality of service and promotion of market share expansion and practically expected to be able to provide input to all employees of BSM KCP Ujungberung regarding the quality of service and promotion of market expansion of bailout products. Hajj that has been successfully carried out by BSM KCP Rancaekek.The conclusion of this study is that there is a significant influence of service quality on the expansion of market share by 53.3% with a strong correlation of 0.730 and through t test, where t counts at 8.245 (> t table), then H_0 is rejected and H_i is accepted. Furthermore, there is a significant influence of promotion on the expansion of market share by 30.3% with a moderate / sufficient correlation of 0.550 through t test, where t counts is 4.219 (> t table), then H_ (0) is rejected and H_i is accepted. Then there is a significant influence of service quality and promotion simultaneously to the expansion of market share by 60.6% and a strong correlation of 0.784 and through Test F, where F count is 67.023 (> F table), then 〖H〗 _ ( 0) rejected and H_i accepted.

k-Submodular maximization with two kinds of constraints

2020 ◽  
pp. 2150036
Author(s):  
Ganquan Shi ◽  
Shuyang Gu ◽  
Weili Wu
Keyword(s):  
Approximation Algorithm ◽  
Submodular Function ◽  
Approximation Ratio ◽  
Size Difference ◽  
Total Size ◽  
Difference Constraints ◽  
Submodular Maximization ◽  
Individual Size

[Formula: see text]-submodular maximization is a generalization of submodular maximization, which requires us to select [Formula: see text] disjoint subsets instead of one subset. Attracted by practical values and applications, we consider [Formula: see text]-submodular maximization with two kinds of constraints. For total size and individual size difference constraints, we present a [Formula: see text]-approximation algorithm for maximizing a nonnegative k-submodular function, running in time [Formula: see text] at worst. Specially, if [Formula: see text] is multiple of [Formula: see text], the approximation ratio can reduce to [Formula: see text], running in time [Formula: see text] at worst. Besides, this algorithm can be applied to [Formula: see text]-bisubmodular achieving [Formula: see text]-approximation running in time [Formula: see text]. Furthermore, if [Formula: see text] is multiple of 2, the approximation ratio can reduce to [Formula: see text], running in time [Formula: see text] at worst. For individual size constraint, there is a [Formula: see text]-approximation algorithm for maximizing a nonnegative [Formula: see text]-submodular function and an nonnegative [Formula: see text]-bisubmodular function, running in time [Formula: see text] and [Formula: see text] respectively, at worst.

scholarly journals How to Build Your Own ASP-based System?!

2021 ◽  
pp. 1-63
Author(s):  
ROLAND KAMINSKI ◽  
JAVIER ROMERO ◽  
TORSTEN SCHAUB ◽  
PHILIPP WANKO
Keyword(s):  
Problem Solving ◽  
Application Programming Interface ◽  
Ease Of Use ◽  
Cross Sectional ◽  
Difference Constraints ◽  
Application Programming ◽  
Set Up ◽  
The One ◽  
High Degree ◽  
Abstract Syntax Trees

Abstract Answer Set Programming, or ASP for short, has become a popular and sophisticated approach to declarative problem solving. Its popularity is due to its attractive modeling-grounding-solving workflow that provides an easy approach to problem solving, even for laypersons outside computer science. However, in contrast to ASP’s ease of use, the high degree of sophistication of the underlying technology makes it even hard for ASP experts to put ideas into practice whenever this involves modifying ASP’s machinery. For addressing this issue, this tutorial aims at enabling users to build their own ASP-based systems. More precisely, we show how the ASP system clingo can be used for extending ASP and for implementing customized special-purpose systems. To this end, we propose two alternatives. We begin with a traditional AI technique and show how metaprogramming can be used for extending ASP. This is a rather light approach that relies on clingo’s reification feature to use ASP itself for expressing new functionalities. The second part of this tutorial uses traditional programming (in Python) for manipulating clingo via its application programming interface. This approach allows for changing and controlling the entire model-ground-solve workflow of ASP. Central to this is clingo’s new Application class that allows us to draw on clingo’s infrastructure by customizing processes similar to the one in clingo. For instance, we may apply manipulations to programs’ abstract syntax trees, control various forms of multi-shot solving, and set up theory propagators for foreign inferences. A cross-sectional structure, spanning meta as well as application programming, is clingo’s intermediate format, aspif, that specifies the interface among the underlying grounder and solver. We illustrate the aforementioned concepts and techniques throughout this tutorial by means of examples and several nontrivial case studies. In particular, we show how clingo can be extended by difference constraints and how guess-and-check programming can be implemented with both meta and application programming.

scholarly journals Accelerated cardiac cine MRI using locally low rank and finite difference constraints

2016 ◽  
Vol 34 (6) ◽  
pp. 707-714 ◽  
Author(s):  
Xin Miao ◽  
Sajan Goud Lingala ◽  
Yi Guo ◽  
Terrence Jao ◽  
Muhammad Usman ◽  
...  
Keyword(s):  
Finite Difference ◽  
Low Rank ◽  
Cine Mri ◽  
Cardiac Cine ◽  
Difference Constraints

scholarly journals Monotone Temporal Planning: Tractability, Extensions and Applications

2014 ◽  
Vol 50 ◽  
pp. 447-485 ◽  
Author(s):  
M. Cooper ◽  
F. Maris ◽  
P. Régnier
Keyword(s):  
Polynomial Time ◽  
Temporal Information ◽  
Planning Problem ◽  
Temporal Planning ◽  
Concurrent Execution ◽  
Difference Constraints ◽  
Potential Applications ◽  
Polynomially Solvable ◽  
Solvable Class ◽  
Planning Problems

This paper describes a polynomially-solvable class of temporal planning problems. Polynomiality follows from two assumptions. Firstly, by supposing that each sub-goal fluent can be established by at most one action, we can quickly determine which actions are necessary in any plan. Secondly, the monotonicity of sub-goal fluents allows us to express planning as an instance of STP≠ (Simple Temporal Problem with difference constraints). This class includes temporally-expressive problems requiring the concurrent execution of actions, with potential applications in the chemical, pharmaceutical and construction industries. We also show that any (temporal) planning problem has a monotone relaxation which can lead to the polynomial-time detection of its unsolvability in certain cases. Indeed we show that our relaxation is orthogonal to relaxations based on the ignore-deletes approach used in classical planning since it preserves deletes and can also exploit temporal information.

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