Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Stochastic Programming (SP) has long been considered a ll-justified yet computationally challenging paradigm for practical applications. Computational studies in the literature often involve approximating a large number of scenarios by using a small number of scenarios to be processed via deterministic solvers, or running Sample Average Approximation on some genre of high performance machines so that statistically acceptable bounds can be obtained.   show that for a class of stochastic linear programming problems, an alternative approach known as Stochastic Decomposition (SD) can provide solutions of similar quality in far less computational time using ordinary desktop or laptop machines of today. In addition to these compelling computational results,  provide a stronger convergence result for SD, and introduce a new solution concept that  call the compromise decision. This new concept is attractive for algorithms that call for multiple replications in sampling-based convex optimizati...
['Suvrajeet Sen', 'Yifan Liu']
I am writing a research paper on a idea Mitigating Uncertainty via Compromise Decisions in Two-Stage Stochastic Linear Programming: Variance Reduction
[{"from": "human", "value": "I am writing a research paper on a idea Mitigating Uncertainty via Compromise Decisions in Two-Stage Stochastic Linear Programming: Variance Reduction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Stochastic Programming (SP) has long been considered a ll-justified yet computationally challenging paradigm for practical applications. Computational studies in the literature often involve approximating a large number of scenarios by using a small number of scenarios to be processed via deterministic solvers, or running Sample Average Approximation on some genre of high performance machines so that statistically acceptable bounds can be obtained.   show that for a class of stochastic linear programming problems, an alternative approach known as Stochastic Decomposition (SD) can provide solutions of similar quality in far less computational time using ordinary desktop or laptop machines of today. In addition to these compelling computational results,  provide a stronger convergence result for SD, and introduce a new solution concept that  call the compromise decision. This new concept is attractive for algorithms that call for multiple replications in sampling-based convex optimizati..."}]