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Existence of solutions in non-convex dynamic programming and optimal investment

机译:非凸动态规划中解决方案的存在与最优投资

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摘要

We establish the existence of minimizers in a rather general setting of dynamic stochastic optimization in finite discrete time without assuming either convexity or coercivity of the objective function. We apply this to prove the existence of optimal investment strategies for non-concave utility maximization problems in financial market models with frictions, a first result of its kind. The proofs are based on the dynamic programming principle whose validity is established under quite general assumptions.
机译:我们在有限的离散时间内在动态随机优化的相当一般的设置中建立了极小值的存在,而没有假设目标函数的凸性或矫顽性。我们以此来证明存在摩擦的金融市场模型中非凹型效用最大化问题的最优投资策略的存在,这是同类问题的第一个结果。证明基于动态编程原理,其有效性是在相当普遍的假设下确定的。

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