20
Oct 17

## Optimization with constraints: economic and financial examples

The economic and financial examples below provide ample motivation for using the optimization theory with constraints. For geometry, check out the post on level sets, isoquants and indifference curves.

## Examples in Economics

Consumption theory. Let $u(x,y)$ be a utility function of consuming two goods $x,y.$ Denote $p_x,\ p_y$ their prices and $M$ the amount available for consumption. Then the budget constraint is $p_xx+p_yy=M.$ The utility maximization problem is to

(1) maximize $u(x,y)$ subject to $p_xx+p_yy=M.$

The dual problem to the utility maximization problem is the expenditure minimization problem:

(2) minimize expenditure $p_xx+p_yy$ subject to the desired level of consumption $u(x,y)=const$

where the $const$ is chosen by the consumer.

Production theory. Let $f(x,y)$ be a production function, that is, $x,y$ are two inputs (for example, capital and labor) and the value $f(x,y)$ gives the output produced using these inputs. $M$ in this context means the budget available for employing the inputs. The output maximization problem is to

(3) maximize $f(x,y)$ subject to the cost constraint $p_xx+p_yy=M.$

This is absolutely similar to (1). Moreover, often both $u(x,y)$ and $f(x,y)$ are modeled as the Cobb-Douglas function $Ax^\alpha y^\beta.$

It's easy to formulate the dual problem of cost minimization:

(4) minimize the cost of production $p_xx+p_yy$ subject to the desired level of output $f(x,y)=const$

where the $const$ is chosen by the producer.

## Examples in Finance

Now we consider investor optimization problems. There are $m$ stocks and the stock returns are denoted $R_1,...,R_m.$ They are random because they are not predictable. The investor has initially $M$ dollars to invest in these stocks. Suppose he chooses to invest $M_1$ in stock 1,..., $M_m$ in stock $m,$ so that the initial investment is split as $M_1+...+M_m=M.$ Dividing this equation through by $M$ we get $M_1/M+...+M_m/M=1.$ Here $s_i=M_i/M$ are called shares of the total investment. They obviously satisfy

(5) $0 and $s_1+...+s_m=1.$

Working with shares is better because they don't depend on $M$ and the results are equally applicable to small and large investors. Shares of investment are determined at the time investment is made and are deterministic. The total return on the portfolio is defined by $R=s_1R_1+...+s_mR_m.$

Riskiness of the portfolio is measured by standard deviation $\sigma (R).$ The basic fact is that a higher expected return $ER$ is associated with a higher risk $\sigma (R).$ Now we can state two optimal portfolio choice problems:

(6) maximize $ER$ subject to the chosen level of risk $\sigma (R)=const$

and

(7) minimize risk $\sigma (R)$ subject to the chosen level of expected return $ER=const.$

In both cases the investor varies the shares $s_{i}$ so as to obtain the best allocation of his money. The constants are chosen subjectively. A more objective problem obtains if we use what is called an expected return per unit of risk: $\frac{ER}{\sigma (R)}.$ Instead of the above two problems we can solve the maximization problem

(8) $\max \frac{ER}{\sigma (R)}$

In addition to explicitly written constraints in (6)-(7) we remember that the shares satisfy (5), which contains inequality constraints.

17
Sep 17

## The Cobb-Douglas function and level sets

Here and here I started to discuss topics in optimization. This post is a preparatory step to the next topic: the Lagrange method.

The Cobb–Douglas production function made a big splash in economics. It is still used a lot. In case of just two inputs, capital and labor, its definition is

$f(K,L)=AK^\alpha L^\beta.$

For simplicity, in the videos below I assume $A=1$. We discuss the definition, homogeneity and the reason for choosing a multiplicative form.

Video 1. Cobb-Douglas production function

It is important to realize that the same function is used for modeling preferences. That is, the utility of consuming a pair of goods is measured by the function

$f(G_1,G_2)=AG_1^\alpha G_2^\beta.$

Thus, everything that has been said about production functions, applies to utility functions as well.

## Level sets, isoquants and indifference curves

Let $z=f(x,y)$ be a function of two arguments. For a fixed constant $c$, the level set $\{(x,y):\ f(x,y)=c\}$ is the set of all pairs $(x,y)$ at which the value of the function is the given constant. Geometrically, $z=f(x,y)$ is a surface in the three-dimensional space. We cut it by a horizontal plane at height $z=c$. In the intersection of the plane and surface we get a curve. This curve, projected onto the $(x,y)$ plane, gives the level set.

Level sets, isoquants and indifference curves define equivalence relations, see the definition and properties here.

The next video explains that isocosts, isoquants and indifference curves are all level sets for certain functions.

Video 2. Level sets, isoquants and indifference curves