Optimisation Problem (Global extreme points)

In summary: So x = 1 minimises f(x).In summary, the conversation is about finding the global extreme point of the function f(x)=e^(x-1) - x and the confusion over whether x or f(x) should be minimized. The solution concludes that x = 1 minimizes f(x) and the conversation confirms that this is correct.
  • #1
Foosey96
1
0
Hi there everyone, wonder if anyone can help as I'm a bit confused.
Ive been asked to find the global extreme point of f(x)=e^(x-1) - x.
I have checked my answer against the solution and am correct and my working is as follows:
f'(x) = e^(x-1) - 1 = 0. Therefore (x-1)=ln1 (which = 0) therefore x = 1.
f''(x) = e(x-1). sub in x=1 and f''(x) = 1 which is > 0 hence the extreme point is a minimum.
The solution however then goes on to conclude that x*=1 minimizes f(x)
This is what I am confused by as I thought minimum points by their nature were the y-values not the x values and hence x* should actually be e^(1-1) - 1 = 0 and so x*=0 minimizes f(x)??
Thanks for any help you can give that final conclusion has just thrown me off. Thanks everyone!
 
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  • #2
Foosey96 said:
Hi there everyone, wonder if anyone can help as I'm a bit confused.
Ive been asked to find the global extreme point of f(x)=e^(x-1) - x.
I have checked my answer against the solution and am correct and my working is as follows:
f'(x) = e^(x-1) - 1 = 0. Therefore (x-1)=ln1 (which = 0) therefore x = 1.
f''(x) = e(x-1). sub in x=1 and f''(x) = 1 which is > 0 hence the extreme point is a minimum.
The solution however then goes on to conclude that x*=1 minimizes f(x)
This is what I am confused by as I thought minimum points by their nature were the y-values not the x values and hence x* should actually be e^(1-1) - 1 = 0 and so x*=0 minimizes f(x)??
Thanks for any help you can give that final conclusion has just thrown me off. Thanks everyone!

What they said is correct. To say that x = 1 minimises f(x) is to say that when x = 1 IN the function, then the function value will be at its minimum.

Here the minimum value is f(1) = e^(1-1) - 1 = -1.
 

FAQ: Optimisation Problem (Global extreme points)

What is an optimization problem?

An optimization problem is a type of mathematical problem where the goal is to find the best possible solution from a set of possible solutions. This is typically achieved by finding the optimal value for a given objective function, while satisfying any constraints imposed on the problem.

What are global extreme points in an optimization problem?

Global extreme points, also known as global extrema, are the highest and lowest points of an objective function in an optimization problem. These points represent the best possible solutions to the problem and are not limited to a specific region or subset of the problem space.

How do you find global extreme points in an optimization problem?

Finding global extreme points in an optimization problem involves using mathematical methods and algorithms to evaluate the objective function at different points in the problem space and determine the optimal values. This process may involve iterative methods, gradient descent, or other techniques depending on the specific problem.

What is the significance of global extreme points in an optimization problem?

The global extreme points in an optimization problem are significant because they represent the best possible solutions to the problem. By finding these points, it is possible to determine the optimal value for the objective function and make informed decisions about the problem at hand.

Can an optimization problem have multiple global extreme points?

Yes, it is possible for an optimization problem to have multiple global extreme points. This is especially true in complex problems with multiple variables and constraints. In these cases, there may be more than one optimal solution that meets the criteria for the best possible outcome.

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