Calculus Book for Mathematicians: Partial Derivatives and Lagrange Multipliers

In summary, Mason recommends a second calculus book written by a mathematician, for mathematicians, that includes partial derivatives and Lagrange multipliers.
  • #1
E'lir Kramer
74
0
Hi everyone. I've just gotten through the first 15 chapters of Spivak's Calculus. Though the rest of the book looks fascinating, I'm currently more interested in studying statistical thermodynamics in light of my new-found math knowledge. I am reading through Dill's Molecular Driving Forces, 2nd ed. The book uses partial derivatives and Lagrange multipliers to derive the Boltzmann distribution, which is the fundamental theorem of statistical thermo. Unfortunately, Spivak hasn't covered this in Calculus.

I loved Spivak's style, and the treatment that these two math topics have gotten in the Dill book is criminal. (It's one of those chapters that is just there to give the teacher something off of which to teach. But I don't have a teacher, and I need a better treatment.) Can anyone recommend me a second calculus book written by a mathemetician, for mathemeticians, that includes partial derivatives and Lagrange multipliers?



Mason
 
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  • #2
I can't remember if these included Lagrange Multipliers, but two books that met the criteria would be Courant Calculus volume II and or Apostol Volume II.
 
  • #4
Thanks all for the suggestions. I'm pleased to see three recommendations for "classics". Since the Courant book is $150, and the Edwards book is $15, I started there. But one day I'd like to add Courant (and Apostol) to my shelf.
 
  • #6
Thanks, Wonk. I'd heard that it's a difficult read, but I'll certainly keep it in mind for further studies. I feel like it's hard to go wrong with some of these guys.

MarneMath: Question about Courant: which book exactly?

Methods of Mathematical Physics, Vol. 2 by Richard Courant and D. Hilbert (Jan 4, 1989)
Differential and Integral Calculus, Vol. 2 by Richard Courant, Edward James McShane and Sam Sloan (Jun 13, 2010)

Neither of these books are named Calculus, but the second one is closer.
 
  • #8
  • #9
There is also a book by Munkres Calculus on Manifolds. Use Apostol vol. 2 & Courant vol. 2 together. Apostol is good but courant is little Rude.
 

FAQ: Calculus Book for Mathematicians: Partial Derivatives and Lagrange Multipliers

What is the purpose of studying partial derivatives and Lagrange multipliers in calculus?

The purpose of studying partial derivatives and Lagrange multipliers in calculus is to understand and analyze the behavior of functions with multiple variables. These concepts allow us to find the maximum and minimum values of a function with respect to one variable while holding the others constant. This is useful in many fields such as economics, physics, and engineering.

How are partial derivatives calculated?

Partial derivatives are calculated by taking the derivative of a function with respect to one variable while treating the other variables as constants. This is similar to finding the slope of a curve in one variable, but in this case, we are finding the slope in a specific direction in multiple dimensions.

What is the significance of Lagrange multipliers in optimization problems?

Lagrange multipliers are used in optimization problems to find the maximum or minimum value of a function subject to a constraint. They allow us to incorporate the constraint into the equation and find the optimal solution without having to solve for multiple variables simultaneously.

How do partial derivatives and Lagrange multipliers relate to each other?

Partial derivatives and Lagrange multipliers are closely related as they both involve finding the extreme values of a function. Partial derivatives are used to find the extreme values in a specific direction, while Lagrange multipliers are used to find the extreme values with a given constraint.

What are some real-life applications of partial derivatives and Lagrange multipliers?

Partial derivatives and Lagrange multipliers have many real-life applications, including optimizing production processes in economics, finding the optimal path for a spaceship in physics, and minimizing energy consumption in engineering. They are also commonly used in machine learning and data analysis to optimize algorithms and models.

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