Math Problem Statement
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f(x, y) = 1 3 x 3 − 3x 2 + y 2 4 + xy + 13x − y + 2
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f(x, y, z) = x 3 − 2x 2 + y 2 + z 2 − 2xy + xz − yz + 3z
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f(x, y) = x 3 + y 5 − 3x − 10y + 4
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f(x, y) = 1 3 x 3 − x − 1 3 y 3 + y
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f(x, y) = 2x 3 + 4y 2 − 2y 4 − 6x
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f(x, y, z) = x 2 y − yez + 2x + z
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f(x, y) = − 1 2 xy + 2 x − 1 y
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f(x, y) = (x 2 − y 2 )e −(x 2+y 2 )/2
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f(x, y) = e −(x 2+y 2 ) (x 2 + 2y 2 )
Solution
You seem to be listing several functions of two or three variables, possibly for analysis or differentiation purposes. These functions involve polynomials, exponentials, and other expressions. Let’s take a closer look at each one:
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This function contains cubic, quadratic, and mixed terms in and , along with constants. -
A function involving three variables () with cubic and quadratic terms, as well as mixed terms like , , and . -
A function with higher-degree terms like and , linear terms, and a constant. -
A simpler function involving cubic and linear terms in both and . -
A function that includes cubic, quadratic, quartic terms in , and linear terms in . -
This function contains a product of , an exponential term , and linear terms in and . -
A rational function involving terms like and , plus a mixed term in . -
This function includes a quadratic term multiplied by an exponential decay factor involving both and . -
Similar to the previous function but with different factors in the polynomial and the exponential.
Would you like help with:
- Finding the partial derivatives of these functions?
- Analyzing the critical points or extrema?
- Evaluating these functions for specific values of , , and ?
- Finding gradient and Hessian matrices?
- Plotting these functions for better visualization?
Tip: When working with multivariable functions, partial derivatives give insights into how the function behaves as each variable changes independently.
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Math Problem Analysis
Mathematical Concepts
Multivariable Calculus
Partial Derivatives
Polynomial Functions
Exponential Functions
Critical Points
Gradient and Hessian Matrices
Formulas
Partial Derivative Formulas: ∂f/∂x, ∂f/∂y, ∂f/∂z
Gradient: ∇f = (∂f/∂x, ∂f/∂y, ∂f/∂z)
Hessian Matrix: Second partial derivatives of f
Theorems
Critical Point Theorem
Second Derivative Test
Chain Rule for Multivariable Functions
Suitable Grade Level
Undergraduate Mathematics (Calculus II/III)
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