Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  develops new bounds on the expectation of a convex-concave saddle function of a random vector with compact domains. The bounds are determined by replacing the underlying distribution by unique discrete distributions, constructed using second-order moment information. The results extend directly to new second moment lor bounds in closed-form for the expectation of a convex function. These lor bounds are better than Jensen's bound, the only previously known lor bound for the convex case, under limited moment information. Application of the second moment bounds to two-stage stochastic linear programming is reported. Computational experiments, using randomly generated stochastic programs, indicate that the new bounds may easily outperform the usual first-order bounds.
['N. C. P. Edirisinghe']
I am writing a research paper on a idea New Second-Order Bounds on the Expectation of Saddle Functions with Applications to Stochastic Linear Programming
[{"from": "human", "value": "I am writing a research paper on a idea New Second-Order Bounds on the Expectation of Saddle Functions with Applications to Stochastic Linear Programming"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  develops new bounds on the expectation of a convex-concave saddle function of a random vector with compact domains. The bounds are determined by replacing the underlying distribution by unique discrete distributions, constructed using second-order moment information. The results extend directly to new second moment lor bounds in closed-form for the expectation of a convex function. These lor bounds are better than Jensen's bound, the only previously known lor bound for the convex case, under limited moment information. Application of the second moment bounds to two-stage stochastic linear programming is reported. Computational experiments, using randomly generated stochastic programs, indicate that the new bounds may easily outperform the usual first-order bounds."}]