A DAC solution could be attempted, but it ends up having too much repeated work. Therefore, we will implement our solution with dynamic programming. Note that this is a 2-D problem (number of investments and amount of money to spend). Thus, we will need a 2-D table. The second step is filling in the base cases. Note that when we don't select any investments, no matter how much money we have to spend (top row), then we get no return on investment. This is a natural base case. The third step is to identify the goal location in the table. This will be the position where we are allowed to include all investments with our full allocation of funds to spend. This will be the lower-right corner. The last step in computing any optimal table is to determine the order in which to fill in the table, keeping in mind that one always needs all the results for the recursive subproblems available to fill in the next location. After the optimal table is computed, a traceback table needs to be added to the implementation so we can find the actual investments that produce the optimal. Recall that traceback tables store the "winning" options at each step and are then used at the end to trace back through these winning options. Your code should return the optimal return on investment number as well as a list of the investment names used to obtain this optimal number