dynamic programming solution
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This article shows why diversified simultaneous retirement funding drawn from accounts with differing tax treatments will save more on taxes than sequential withdrawal. We utilize dynamic programming to quantify the optimal funding under the assumption that the goal is to maximize the total discounted after-tax consumption and bequest amounts. The dynamic programming setting uses the actual tax schedule, considers the required minimum distribution and life expectancy, and constrains consumption with upper and lower bounds. We also find the optimal funding through static diversification, a simple optimization schema. We compare the static and full dynamic programming solutions in two cases, one for a single filer and another for a married joint filer. The static and the full dynamic programming solution differ more in the single filer case than in the married joint filer case. The static optimal withdrawal is close to sequential withdrawal. The full dynamic programming’s optimal withdrawal is close the sequential withdrawal when both types of accounts have a substantial balance.


2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668776
Author(s):  
Zhen Li ◽  
Tao Jing ◽  
Yan Huo

In this article, we consider the physical layer security issue in Internet of Things systems, in which there exist a sensing transceiver pair, a number of candidate nodes, and an eavesdropper. The transceiver pair needs to select a jammer node and a relay node among the candidate nodes so as to preserve the secrecy of the communications. Considering the diversity of candidate channels and the limited available power, it is infeasible to scan all the nodes and find the optimal one. We formulate this jammer and relay selection problem as an optimal stopping problem under a fixed sensing order. Then, through applying dynamic programming solution, we propose a low-complexity approach to obtain the optimal sensing order. The performance of the proposed selection scheme is evaluated through numerical results.


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