Dynamic programming is a method of mathematical optimization, where we deconstruct a larger, harder problem into multiple smaller problems that are easier to solve. Then we reconstruct the optimal solution to the original problem from optimal solutions of the smaller sub-problems, while maintaining optimality on each step. If we introduce uncertainty into the transition between states and the utility while in a certain state, we get stochastic dynamic programming. The aim when solving a problem with dynamic programming is to get the optimal decision rule. We can use this optimization method in many different fields. In this thesis we will look at solving the savings and valuation of bonds problems.
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