I am confused about how multivariable calc works

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SUMMARY

This discussion clarifies the relationship between vectors in three-dimensional space (R^3) and functions of multiple variables, specifically f(x, y, z). The unit vectors i, j, and k represent the directions of the x, y, and z axes, respectively, and are essential for understanding vector calculus. The gradient of a scalar-valued function, obtained by taking partial derivatives with respect to each variable, indicates the direction of the steepest ascent and the rate of change in that direction. This foundational knowledge is crucial for mastering multivariable calculus.

PREREQUISITES
  • Understanding of vectors in R^3
  • Knowledge of functions of multiple variables
  • Familiarity with partial derivatives
  • Basic concepts of gradient in vector calculus
NEXT STEPS
  • Study the concept of gradients in multivariable calculus
  • Learn how to compute partial derivatives for functions of two or more variables
  • Explore the geometric interpretation of vectors and gradients
  • Investigate applications of multivariable calculus in physics and engineering
USEFUL FOR

Students studying multivariable calculus, educators teaching vector calculus, and anyone seeking to understand the application of gradients in higher dimensions.

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Homework Statement


My teacher introduced the third dimension (## R^3 ##) and higher dimensions to my class using vectors. Later on, my teacher introduced functions of two or more variables and now there's no mention of vectors. I am confused as to how vectors (i + j + k) and functions of two or more variables f(x, y, z) are related.

Homework Equations


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The Attempt at a Solution


I'm not sure how to start. Thank you all!
 
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Typical convention is that i is the eigenbasis of x, j is the eigenbasis of y, and k is the eigenbasis of z.

Other than that, it depends on what you're doing. For instance, taking the gradient requires taking the partial of your function, f(x,y,z) with respect to each variable, and multiplying each of those by their respective eigenvector as below:

http://mathworld.wolfram.com/Gradient.html
 
I would not use "eigen" here. The vectors i, j, and k are the unit vectors pointing in the directions of the x, y, and z axes, respectively. The gradient of a scalar valued function points in the direction of fastest increase and its length is the rate of change in that direction.
 

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