Linear programming duality yields e,cient algorithms for solving inverse linear programs. We show that special classes of conic programs admit a similar duality and, as a consequence, establish that the corresponding inverse programs are e,ciently solvable. We discuss applications of inverse conic programming in portfolio optimization and utility function identi0cation. c © 2004 Elsevier B.V. A...

We study the min max optimization problem introduced in [22] for computing policies for batch mode reinforcement learning in a deterministic setting. First, we show that this problem is NP-hard. In the twostage case, we provide two relaxation schemes. The first relaxation scheme works by dropping some constraints in order to obtain a problem that is solvable in polynomial time. The second relax...

e-learning model is examined of three major dimensions. and each dimension has a range of indicators that is effective in optimization and modeling, in many optimization problems in the modeling, target function or constraints may change over time that as a result optimization of these problems can also be changed. if any of these undetermined events be considered in the optimization process, t...

As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization (PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to o...

in this paper proposes a fuzzy multi-objective hybrid genetic and bee colony optimization algorithm(ga-bco) to find the optimal restoration of loads of power distribution network under fault.restoration of distribution systems is a complex combinatorial optimization problem that should beefficiently restored in reasonable time. to improve the efficiency of restoration and facilitate theactivity...

In nanoscale technologies process variability makes it extremely difficult to predict the behavior of manufactured integrated circuits (IC). The problem is especially exacerbated in analog IC where long design cycles, multiple manufacturing iterations, and low performance yields causes only few design to have the volume required to be economically viable. This paper presents a new framework tha...

This work develops a novel two-stage fuzzy optimization method for solving the multiproduct multi-period (MPMP) production planning problem, in which the market demands and some of the inventory costs are assumed to be uncertainty and characterized by fuzzy variables with known possibility distributions. Some basic properties about the MPMP production planning problem are discussed. Since the f...

Robust optimization is a common framework in optimization under uncertainty when the problem parameters are not known, but it is rather known that the parameters belong to some given uncertainty set. In the robust optimization framework the problem solved is a min-max problem where a solution is judged according to its performance on the worst possible realization of the parameters. In many cas...

We study the minmax optimization problem introduced in [6] for computing policies for batch mode reinforcement learning in a deterministic setting. This problem is NP-hard. We focus on the two-stage case for which we provide two relaxation schemes. The first relaxation scheme works by dropping some constraints in order to obtain a problem that is solvable in polynomial time. The second relaxati...