Financial portfolios are often optimized for maximum profit while subject toa constraint formulated in terms of the Conditional Value-at-Risk (CVaR). Thisamounts to solving a linear problem. However, in its original formulation thislinear problem has a very large number of linear constraints, too many to beenforced in practice. In the literature this is addressed by a reformulation ofthe problem using so-called dummy variables. This reduces the large number ofconstraints in the original linear problem at the cost of increasing the numberof variables. In the context of reinsurance portfolio optimization we observethat the increase in variable count can lead to situations where solving thereformulated problem takes a long time. Therefore we suggest a differentapproach. We solve the original linear problem with cutting-plane method: Theproposed algorithm starts with the solution of a relaxed problem and theniteratively adds cuts until the solution is approximated within a presetthreshold. This is a new approach. For a reinsurance case study we show that asignificant reduction of necessary computer resources can be achieved.
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