A faster capacity scaling algorithm for minimum cost submodular flow

2002 ◽  
Vol 92 (1) ◽  
pp. 119-139 ◽  
Author(s):  
Lisa Fleischer ◽  
Satoru Iwata ◽  
S. Thomas McCormick
Keyword(s):  
Minimum Cost ◽  
Capacity Scaling ◽  
Submodular Flow

Capacity scaling algorithm for scalable M-convex submodular flow problems

2003 ◽  
Vol 18 (2) ◽  
pp. 207-218 ◽  
Author(s):  
Satoko Moriguchi ◽  
Kazuo Murota
Keyword(s):  
Flow Problems ◽  
Capacity Scaling ◽  
Submodular Flow

A Capacity Scaling Algorithm for M-convex Submodular Flow

2004 ◽  
pp. 352-367
Author(s):  
Satoru Iwata ◽  
Satoko Moriguchi ◽  
Kazuo Murota
Keyword(s):  
Capacity Scaling ◽  
Submodular Flow

A capacity scaling algorithm for M-convex submodular flow

2004 ◽  
Vol 103 (1) ◽  
pp. 181-202 ◽  
Author(s):  
Satoru Iwata ◽  
Satoko Moriguchi ◽  
Kazuo Murota
Keyword(s):  
Capacity Scaling ◽  
Submodular Flow

A Strongly Polynomial Cut Canceling Algorithm for Minimum Cost Submodular Flow

2005 ◽  
Vol 19 (2) ◽  
pp. 304-320 ◽  
Author(s):  
Satoru Iwata ◽  
S. Thomas McCormick ◽  
Maiko Shigeno
Keyword(s):  
Minimum Cost ◽  
Submodular Flow ◽  
Strongly Polynomial

Capacity scaling algorithm for scalable M-convex submodular flow problems

2003 ◽  
Vol 18 (2) ◽  
pp. 1-1
Author(s):  
Kazuo Murota ◽  
Satoko Moriguchi
Keyword(s):  
Flow Problems ◽  
Capacity Scaling ◽  
Submodular Flow

Fast Cycle Canceling Algorithms for Minimum Cost Submodular Flow*

COMBINATORICA ◽  
2003 ◽  
Vol 23 (3) ◽  
pp. 503-525 ◽  
Author(s):  
Satoru Iwata† ◽  
S. Thomas Mccormick‡ ◽  
Maiko Shigeno§
Keyword(s):  
Minimum Cost ◽  
Submodular Flow

A Strongly Polynomial Algorithm for Minimum Cost Submodular Flow Problems

1989 ◽  
Vol 14 (1) ◽  
pp. 60-69 ◽  
Author(s):  
Satoru Fujishige ◽  
Hans Röck ◽  
Uwe Zimmermann
Keyword(s):  
Polynomial Algorithm ◽  
Minimum Cost ◽  
Strongly Polynomial Algorithm ◽  
Flow Problems ◽  
Submodular Flow ◽  
Strongly Polynomial

Maximum production — minimum cost via value analysis

1963 ◽  
Vol 42 (3) ◽  
pp. 147
Author(s):  
N. Taylor
Keyword(s):  
Minimum Cost ◽  
Value Analysis ◽  
Maximum Production

A generalized fuzzy cost efficiency model

2020 ◽  
Vol 54 (6) ◽  
pp. 1775-1791
Author(s):  
Nazila Aghayi ◽  
Samira Salehpour
Keyword(s):  
Cost Efficiency ◽  
Minimum Cost ◽  
Extensive Literature ◽  
Data Envelopment ◽  
Fuzzy Environment ◽  
Decision Making Unit ◽  
Production Possibility Set ◽  
Fuzzy Cost ◽  
The Cost ◽  
Generalized Cost

The concept of cost efficiency has become tremendously popular in data envelopment analysis (DEA) as it serves to assess a decision-making unit (DMU) in terms of producing minimum-cost outputs. A large variety of precise and imprecise models have been put forward to measure cost efficiency for the DMUs which have a role in constructing the production possibility set; yet, there’s not an extensive literature on the cost efficiency (CE) measurement for sample DMUs (SDMUs). In an effort to remedy the shortcomings of current models, herein is introduced a generalized cost efficiency model that is capable of operating in a fuzzy environment-involving different types of fuzzy numbers-while preserving the Farrell’s decomposition of cost efficiency. Moreover, to the best of our knowledge, the present paper is the first to measure cost efficiency by using vectors. Ultimately, a useful example is provided to confirm the applicability of the proposed methods.

Main approaches to estimating the cost of shareholder equity of oil and gas producers

2020 ◽  
Vol 26 (3) ◽  
pp. 685-697
Author(s):  
O.V. Shimko
Keyword(s):  
Oil And Gas ◽  
Oil Prices ◽  
Minimum Cost ◽  
Free Cash Flow ◽  
Annual Reports ◽  
Comparative Approach ◽  
Economic Activities ◽  
Advantages And Disadvantages ◽  
Oil And Gas Producers ◽  
The Cost

Subject. The study analyzes generally accepted approaches to assessing the value of companies on the basis of financial statement data of ExxonMobil, Chevron, ConocoPhillips, Occidental Petroleum, Devon Energy, Anadarko Petroleum, EOG Resources, Apache, Marathon Oil, Imperial Oil, Suncor Energy, Husky Energy, Canadian Natural Resources, Royal Dutch Shell, Gazprom, Rosneft, LUKOIL, and others, for 1999—2018. Objectives. The aim is to determine the specifics of using the methods of cost, DFC, and comparative approaches to assessing the value of share capital of oil and gas companies. Methods. The study employs methods of statistical analysis and generalization of materials of scientific articles and official annual reports on the results of financial and economic activities of the largest public oil and gas corporations. Results. Based on the results of a comprehensive analysis, I identified advantages and disadvantages of standard approaches to assessing the value of oil and gas producers. Conclusions. The paper describes pros and cons of the said approaches. For instance, the cost approach is acceptable for assessing the minimum cost of small companies in the industry. The DFC-based approach complicates the reliability of medium-term forecasts for oil prices due to fluctuations in oil prices inherent in the industry, on which the net profit and free cash flow of companies depend to a large extent. The comparative approach enables to quickly determine the range of possible value of the corporation based on transactions data and current market situation.

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