max(-x - xy-2y + 2rx + where r is a parameter. Verify equation (3). SM 5. Find the solutions x* (r, s) and y*(r, s) of the problem

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ISBN:9780470458365
Author:Erwin Kreyszig
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Chapter2: Second-order Linear Odes
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Can you help with question 5? Equation (3) I need to verify is attached. 

CHAPTER 3 / STATIC OPTIMIZATION
4. Find the functions x (r) and y"(r) such that x =x*(r) and y = y (r) solve the problem
max(-x – xy – 2y²+2rx + 2ry)
where r is a parameter. Verify equation (3).
SM 5. Find the solutions x*(r, s) and y*(r, s) of the problem
max f (x, y, r, s) =
max(r'x + 3s*y - x - 8y²)
where r and s are parameters. Verify equation (3).
SM 6. (a) Suppose the production function in Example 2 is
F(v,..., Vn) = a¡ In(v1 + 1)+ · + a, In(vn + 1)
where a1, ..., an are positive constants and p > qi/a¡ for i = 1,., n. Find the profit
maximizing choice of input quantities.
(b) Verify the envelope result (3) w.r.t. p, each q1 and each a,.
3.2 Local Extreme Points
Suppose one is trying to find the maximum of a function that is not concave, or a minimum
of a function that is not convex. Then Theorem 3.1.2 cannot be used. Instead, one possible
procedure is to identify local extreme points, and then compare the values of the function
at different local extreme points in the hope of finding a global maximum (or minimum).
The point x is a local maximum point of f in S if ƒ(x) < ƒ(x*)forall x in S sufficiently
close to x*. More precisely, the requirement is that there exists a positive number r such that
f (x) < f(x*) for all x in S with ||x- x' || <r
(*)
Transcribed Image Text:CHAPTER 3 / STATIC OPTIMIZATION 4. Find the functions x (r) and y"(r) such that x =x*(r) and y = y (r) solve the problem max(-x – xy – 2y²+2rx + 2ry) where r is a parameter. Verify equation (3). SM 5. Find the solutions x*(r, s) and y*(r, s) of the problem max f (x, y, r, s) = max(r'x + 3s*y - x - 8y²) where r and s are parameters. Verify equation (3). SM 6. (a) Suppose the production function in Example 2 is F(v,..., Vn) = a¡ In(v1 + 1)+ · + a, In(vn + 1) where a1, ..., an are positive constants and p > qi/a¡ for i = 1,., n. Find the profit maximizing choice of input quantities. (b) Verify the envelope result (3) w.r.t. p, each q1 and each a,. 3.2 Local Extreme Points Suppose one is trying to find the maximum of a function that is not concave, or a minimum of a function that is not convex. Then Theorem 3.1.2 cannot be used. Instead, one possible procedure is to identify local extreme points, and then compare the values of the function at different local extreme points in the hope of finding a global maximum (or minimum). The point x is a local maximum point of f in S if ƒ(x) < ƒ(x*)forall x in S sufficiently close to x*. More precisely, the requirement is that there exists a positive number r such that f (x) < f(x*) for all x in S with ||x- x' || <r (*)
Later we shall givé more efficient methods for finding extreme
points of such functions.
Envelope Theorem for Unconstrained Maxima
The objective function in economic optimization problems usually involves parameters like
prices in addition to choice variables like quantities. Consider an objective function with a
parameter vector r of the form f(x, r) = f(x1,.. , Xn, ri,..., rk), where x ESCR" and
re R. For each fixed r suppose we have found the maximum of f(x, r) when x varies in
S. The maximum value of f (x, r) usually depends on r. We denote this value by f*(r) and
call f the value function. Thus,
%3D
(1),f
XES
= max f(x, r)
(the value function)
()
The vector x that maximizes f (x, r) depends on r and is denoted by x* (r).' Thenf*(r) =
f(x*(r), r).
How does f*(r) vary as the jth parameter r; changes? Provided that f* is differentiable
we have the following so-called envelope result:
(1), fe
raf (x, r)
j = 1, ., k
(3)
%3D
(1),X=1r
Note that on the right-hand side we differentiate f w.r.t. its (n + j)th argument, which is
ri, and evaluate the derivative at (x*(r), r).
When the parameter r; changes, f*(r) changes for two reasons. First, a change in
r; changes x*(r). Second, f(x*(r), r) changes directly because the variable r; changes.
1 There may be several choices of x that maximize f (x, r) for a given parameter vector r. Then we
let x* (r) denote one of these choices, and try to select x for different values of r so that x*(r) is a
differentiable function of r.
10:30 PM
4/3/2021
Transcribed Image Text:Later we shall givé more efficient methods for finding extreme points of such functions. Envelope Theorem for Unconstrained Maxima The objective function in economic optimization problems usually involves parameters like prices in addition to choice variables like quantities. Consider an objective function with a parameter vector r of the form f(x, r) = f(x1,.. , Xn, ri,..., rk), where x ESCR" and re R. For each fixed r suppose we have found the maximum of f(x, r) when x varies in S. The maximum value of f (x, r) usually depends on r. We denote this value by f*(r) and call f the value function. Thus, %3D (1),f XES = max f(x, r) (the value function) () The vector x that maximizes f (x, r) depends on r and is denoted by x* (r).' Thenf*(r) = f(x*(r), r). How does f*(r) vary as the jth parameter r; changes? Provided that f* is differentiable we have the following so-called envelope result: (1), fe raf (x, r) j = 1, ., k (3) %3D (1),X=1r Note that on the right-hand side we differentiate f w.r.t. its (n + j)th argument, which is ri, and evaluate the derivative at (x*(r), r). When the parameter r; changes, f*(r) changes for two reasons. First, a change in r; changes x*(r). Second, f(x*(r), r) changes directly because the variable r; changes. 1 There may be several choices of x that maximize f (x, r) for a given parameter vector r. Then we let x* (r) denote one of these choices, and try to select x for different values of r so that x*(r) is a differentiable function of r. 10:30 PM 4/3/2021
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