# Point Optimization

Point optimization requires solving problems concerning the maximization or minimization of a real function by checking all available alternatives (variables), and thereby discovering the best available solution.

Suppose you are looking for the optimal investment strategy for your portfolio. You are able to invest in stocks and bonds. The "function" is the possible future return, and the risk of the investment decision. You are restricted by the amount of money you are able to invest. The optimization process would look for the allocation of stock and bond investments in your portfolio that delivers the highest return at the lowest risk. One possible optimal solution might be to invest 30% of your money in stocks and 70% in bonds.

The "point" in point optimization refers to the fact that only a single alternative is allowed as the solution to the problem. A set of alternatives (variables) is not permitted. Thus, in the above example, point optimization would not permit a solution such as: "the highest return at the lowest risk is achieved by investing 25% to 35% of your money in stocks, and 65% to 75% in bonds." Only a single alternative would be recognized, such as "25% in stocks, 75% in bonds," or "26% in stocks, 74% in bonds" and so on.