Distributed Computational Framework for Large-Scale Stochastic Convex Optimization
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors
This paper introduces a distributed computational framework for stochastic curved optimization problems by decomposing the large-scale scenarios into distributed scenario programs that exchange a certain number of scenarios among themselves in order to compute local decisions and developed a so so-called soft communication scheme to reduce the required communication among subproblems.
However, the authors could work on the following points to improve the overall quality of the paper.
- The introduction and outline should provide more "teasers" to raise the interest of the reader. Why is this study necessary?
- Provide a detailed overview of the base assumptions in sections 2 and 3.
-The related work section may be introduced before the conclusion and create a link between the proposed scheme and existing schemes. Also, some discussion needs to be made in a comparative way with other related work.
- some of the references are not cited properly in the text and the numbering is not in sequence.
- There are some misspelling and grammar mistakes in the manuscript. The authors may let an English native speaker do the final proof-reading.
Finally and considering all the mentioned aspects, I recommend a minor revision of the paper.
Author Response
We thank the reviewer very much for his constructive feedback. We have carefully addressed all the comments. Please find the detailed responses for each comment attached.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
This paper presented a rigorous approach to distributed stochastic optimization using the scenario-based approximation for large-scale linear systems with local and common uncertainty sources. The paper's topic is the brank new method to solve the Large-Scale Stochastic Convex Optimization problems. The paper is explained clearly and logic for the reader easy to understand, the topic is interesting, in line with recent research trends, and the approach could improve the annoying. In my regard, this paper represents a good piece of work. Nevertheless, some critical issues should be considered to make the work publishable: The current version of this manuscript includes concerns showing as follows.
- The references all are too old, please update it and add more recent publications on the stochastic optimization algorithms.
- Conclusions can be improved. This reviewer strongly suggests that the authors clearly explain what the significant findings are and why your paper is significant. Some of the essential quantitative results should be reported to better demonstrate the findings of the carried-out work. The future directions in this field might be clearly mentioned in the conclusion part.
- This paper has checked the similar and paragram by ithenticate software, and the similar rate is is hight, it should be revised carefully. Could the author revise this paper and try to reduce the similarity is less than 25%?
Author Response
We thank the reviewer very much for his positive and constructive feedback. We have carefully addressed all the comments. Please find the detailed responses for each comment attached.
Author Response File: Author Response.pdf
Reviewer 3 Report
The article is interesting and the own research contribution is visible. The literature review is small, only 25 items. It should be extended.
Author Response
We thank the reviewer very much for his positive and constructive feedback. We have carefully addressed this comment by adding additional citations into the paper and correctly pointing out their connections to our work in the Introduction (Related Works).
