The shortest distance from the origin to the hyperbola is 5 units.
Solución
We wish to minimize the distance from the origin to a point on the hyperbola
Since the distance is minimized when its square is minimized, we set up the function to be minimized as
We introduce a Lagrange multiplier and define the constraint function
According to the method of Lagrange multipliers, we must have
Step 1. Compute Gradients
First, compute the gradient of :
Thus,
Next, compute the gradient of :
So,
Step 2. Set Up the Lagrange Equations
We require:
Divide the first two equations by 2 to simplify:
Step 3. Expressing Relations
Rearrange equation (1):
which gives
Assuming and are not zero (we will consider the zero cases later), we solve for :
Now rearrange equation (2):
or
Solving for (again assuming ):
Step 4. Find by Consistency
Substitute the expression for from (3) into (4):
Assuming we cancel to obtain:
Multiply both sides by :
Expand the right-hand side:
Thus, the equation becomes:
Subtract from both sides:
which simplifies to:
Solve the quadratic equation using the quadratic formula:
Thus,
This gives two solutions:
Step 5. Investigate Each Value
Case 1:
Using equation (3):
So we have:
Substitute into the constraint:
Simplify the above:
Solve for :
Thus,
and correspondingly,
The squared distance from the origin is:
so the shortest distance is:
Case 2:
Using equation (3) again:
Substitute into the constraint:
Simplify term by term:
Combine the terms:
so we have:
This implies:
which is not possible for a real . Hence, does not yield a valid solution.
Step 6. Conclusion
The only valid solution corresponds to , giving the points:
The shortest distance from the origin to the hyperbola is:
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The Deep Dive
To find the shortest distance from the origin to the hyperbola defined by , we can apply the Lagrange multiplier method. The distance from the origin can be expressed as . To simplify our calculations, we can minimize instead of .
The constraint we need to satisfy is .
We will set up the system of equations using the Lagrange multipliers :
Compute the gradients:
Setting the gradients equal gives us:
Additionally, we have the constraint:
Now, we can evaluate equations (1) and (2) to express in terms of and and solve the resultant equations. Substitute possible values, starting with and solving case-by-case or substituting to simplify values.
This yields critical points, which we substitute back into equation (3) to find , ensuring to consider all solutions obtained from the multipliers as we could have multiple local minima and maxima.
Following these substitutions and simplifications will eventually lead us to the point(s) where the shortest distance occurs. The process reveals the minimum distance point on the hyperbola accordingly to the distance formula we’ve laid out.
After solving the resulting equations from the above process, one should find the point on the hyperbola nearest to the origin, calculate the corresponding minimum distance . The rigorous checking of all calculations guarantees avoiding common pitfalls, like failing to evaluate all candidates from Lagrange’s setup.
In this case, through adequate calculations, we find that the relevant point on the hyperbola is leading to the shortest distance being .