Partial Derivatives: Find $\frac{\partial f}{\partial x}$ for $y=x^2+2x+3$

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Discussion Overview

The discussion revolves around the differentiation of the function \(f(x) = x^2 + 2x + 3\) with respect to \(x\) and the implications of treating \(y\) as a function of \(x\) or as a separate variable. Participants explore the concept of partial derivatives in the context of optimization problems involving multiple variables.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • Some participants state that \(\frac{\partial f}{\partial x} = 2x + 2\) for the function \(f(x)\).
  • Others question the use of partial derivatives, suggesting that if \(f\) is a function of a single variable, the derivative should be expressed as \(\frac{df}{dx}\) instead.
  • One participant proposes that if \(y\) is treated as a separate variable, then \(\frac{\partial f}{\partial x} = 2\) when \(f(x,y) = y + 2x + 3\), but if \(y\) is a function of \(x\), the derivative becomes \(\frac{\partial f}{\partial x} = \frac{\partial y}{\partial x} + 2 = 2x + 2\).
  • Another participant emphasizes the need for clarity in notation and asks for elaboration on the original question regarding differentiation.
  • Some participants introduce the topic of optimization, discussing whether to treat problems as single-variable or multi-variable based on the relationships between variables.
  • There is mention of constrained versus unconstrained optimization, with a suggestion that constraints can simplify the problem by eliminating variables.

Areas of Agreement / Disagreement

Participants express differing views on the use of partial derivatives versus ordinary derivatives, and there is no consensus on the best approach to differentiate the function when considering \(y\) as a variable. The discussion on optimization also reflects multiple competing views on how to approach the problem.

Contextual Notes

Participants highlight the importance of consistent notation and clarity in defining variables. The discussion touches on the complexity of optimization problems, particularly regarding the relationships between multiple variables and the implications for differentiation.

Who May Find This Useful

This discussion may be useful for students and practitioners in mathematics, physics, and engineering who are exploring differentiation techniques, particularly in the context of optimization and multi-variable calculus.

OhMyMarkov
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Hello Everyone!

This has been confusing me a lot: consider a function $f(x) = x^2 + 2x + 3$. Now, $\frac{\partial f}{\partial x} = 2x + 2$. Now, someone tells me that $y = x^2$. What is $\frac{\partial f}{\partial x}$ now?
 
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OhMyMarkov said:
Hello Everyone!

This has been confusing me a lot: consider a function $f(x) = x^2 + 2x + 3$. Now, $\frac{\partial f}{\partial x} = 2x + 2$. Now, someone tells me that $y = x^2$. What is $\frac{\partial f}{\partial x}$ now?

Hi OhMyMarkov, :)

What method did you use to differentiate \(f(x) = x^2 + 2x + 3\) ?
 
OhMyMarkov said:
Hello Everyone!

This has been confusing me a lot: consider a function $f(x) = x^2 + 2x + 3$. Now, $\frac{\partial f}{\partial x} = 2x + 2$. Now, someone tells me that $y = x^2$. What is $\frac{\partial f}{\partial x}$ now?

It would help if you used a consistent notation, and were more explicit with the question.

If you are asking: if \(f(x,y)=y+2x+3\) what is \(\frac{\partial f}{\partial x}\)? Then the answer is:

\(\frac{\partial f}{\partial x}=2\)

but we are treating \(y\) as a separate variable from \(x\), if it is a function of \(x\) we have:

\(\frac{\partial f}{\partial x}=\frac{\partial y}{\partial x}+2=2x+2\)

CB
 
OhMyMarkov said:
Hello Everyone!

This has been confusing me a lot: consider a function $f(x) = x^2 + 2x + 3$. Now, $\frac{\partial f}{\partial x} = 2x + 2$. Now, someone tells me that $y = x^2$. What is $\frac{\partial f}{\partial x}$ now?

Sudharaka said:
Hi OhMyMarkov, :)

What method did you use to differentiate \(f(x) = x^2 + 2x + 3\) ?

Hi OhMyMarkov,

I think CaptainBlack has given you a complete explanation about all you need to know. :)

Looking at your question what I thought was, you have differentiated \(f(x) = x^2 + 2x + 3\) with respect to \(x\) either by using the definition of the derivative or by using derivatives of elementary functions. So you want to find out how to differentiate \(y=x^2\) using the same method.

Can you please elaborate more about what your question is?

Kind Regards,
Sudharaka.
 
OhMyMarkov said:
Hello Everyone!

This has been confusing me a lot: consider a function $f(x) = x^2 + 2x + 3$. Now, $\frac{\partial f}{\partial x} = 2x + 2$. Now, someone tells me that $y = x^2$. What is $\frac{\partial f}{\partial x}$ now?

Your notation isn't quite standard. If f(x) is a function of the single varable, x, then the derivative should be written $\frac{df}{dx}= 2x+ 2$, an "ordinary" derivative, not a partial derivative.

If, further, $y= x^2$ so that f(x,y)= y+ 2x+ 3, we have $\frac{\partial f}{\partial x}= 2$ and $\frac{\partial f}{\partial y}= 1$. But knowing that $y= x^2$, so that $\frac{dy}{dx}= 2x$, we can use the chain rule to regain $\frac{df}{dx}$$= \frac{\partial f}{\partial y}\frac{dy}{dx}+ \frac{\partial f}{\partial x}\frac{dx}{dx}$$= 1(2x)+ 2(1)= 2x+ 2$ as before.
 
I'm asking about this in relation to a problem of a more general scope, optimization. If we have more than one variable, x1, x2, ... xK, with a subtle relationship between the variables (kind of like how the expectation of an RV is related to the trace of a covariance matrix, for e.g.). When trying to find a minimum of a function, should we treat the problem as single-variable or multiple-variable.
 
OhMyMarkov said:
I'm asking about this in relation to a problem of a more general scope, optimization. If we have more than one variable, x1, x2, ... xK, with a subtle relationship between the variables (kind of like how the expectation of an RV is related to the trace of a covariance matrix, for e.g.). When trying to find a minimum of a function, should we treat the problem as single-variable or multiple-variable.

Multi-variable constrained optimisation.

Unconstrained if you can use the side condition/s to eliminate one of the variable from the objective.

CB
 

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