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In this paper we propose a variant of the random coordinate descent method for solving linearly constrained convex optimization problems with composite objective functions. If the smooth part of the objective function has Lipschitz continuous gradient, then we prove that our method obtains an ϵ-optimal solution in $\mathcal{O}(n^{2}/\epsilon)$ iterations, where n is the number of blocks. For...
This paper focuses on the application of risk-based models to cogeneration system planning in the presence of uncertainties. Starting from the electrical and thermal load patterns, and the electricity and gas prices for a reference year, the uncertain variables for the successive years of analysis (demand and energy prices) are represented by using average values and covariance matrices, taking into...
It is the core problem in building a fuzzy classification system to extract an optimal group of fuzzy classification rules from fuzzy data set. A new kind of algorithm is proposed for fuzzy rules' generating in this work. The idea behind the algorithm is mainly based on both, concepts of data mining and particle swarm optimization (PSO) algorithm. A fuzzy information entropy based measure which generalizes...
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