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Aprenda Partial Derivatives | Section
Understanding Multivariate Calculus

bookPartial Derivatives

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When working with functions of several variables, such as f(x,y)f(x, y), the idea of a derivative extends naturally, but with a key difference: you can measure how the function changes with respect to one variable while keeping the others fixed. This is known as taking a partial derivative.

To compute the partial derivative of a function with respect to a specific variable, you treat all other variables as constants. For example, consider the function f(x,y)=x2y+3xy2f(x, y) = x^2y + 3xy^2. If you want to find the partial derivative with respect to xx, denoted as fx\frac{\raisebox{1pt}{$∂f$}}{\raisebox{-1pt}{$∂x$}}, you treat yy as a constant and differentiate as you would in single-variable calculus. The result is:

  • The derivative of x2yx^2y with respect to xx is 2xy2xy (since yy is constant);
  • The derivative of 3xy23xy^2 with respect to xx is 3y23y^2 (again, yy is constant).

So, fx=2xy+3y2\frac{\raisebox{1pt}{$∂f$}}{\raisebox{-1pt}{$∂x$}} = 2xy + 3y^2.

Similarly, to find the partial derivative with respect to yy, denoted as fy\frac{\raisebox{1pt}{$∂f$}}{\raisebox{-1pt}{$∂y$}}, treat xx as constant:

  • The derivative of x2yx^2y with respect to yy is x2x^2;
  • The derivative of 3xy23xy^2 with respect to yy is 6xy6xy.

So, fy=x2+6xy\frac{\raisebox{1pt}{$∂f$}}{\raisebox{-1pt}{$∂y$}} = x^2 + 6xy.

This process can be generalized for any function of several variables. The notation for partial derivatives often uses the curly "" symbol to distinguish from ordinary derivatives. Geometrically, the partial derivative with respect to a variable at a point gives the slope of the tangent line to the curve you obtain by varying that variable alone, holding the others fixed.

Partial derivatives are foundational to multivariate calculus, enabling you to analyze how a function changes in each direction independently.

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import sympy as sp x, y = sp.symbols('x y') f = x**2 * y + 3 * x * y**2 # Compute partial derivatives symbolically df_dx = sp.diff(f, x) df_dy = sp.diff(f, y) # Evaluate at the point (x, y) = (1, 2) df_dx_at_1_2 = df_dx.subs({x: 1, y: 2}) df_dy_at_1_2 = df_dy.subs({x: 1, y: 2}) print("Symbolic partial derivative ∂f/∂x:", df_dx) print("Value at (1, 2):", df_dx_at_1_2) print("Symbolic partial derivative ∂f/∂y:", df_dy) print("Value at (1, 2):", df_dy_at_1_2)
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Which of the following correctly describes how to compute the partial derivative of f(x,y)f(x, y) with respect to xx?

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Seção 1. Capítulo 3
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