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week 7
MTH-261 Week 7 (October 6-12)
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week 7 >
outline
Outline
14.6 Directional Derivatives and the Gradient Vector
recommended exercises
2, 5, 9, 13, 17, 23, 33, 43, 47, 55, 59, 61, 63
learning objectives
determine the directional derivative in a given direction for a function
of two variables
determine the gradient vector of a given real-valued function
explain the significance of the gradient vector with regard to direction
of change along a surface
use the gradient to find the tangent to a level curve of a given function
calculate directional derivatives and gradients in three dimensions
additional notes
Note the cool properties of the gradient
if the gradient of a function is zero (the vector) at a given
point then the directional derivatives are zero (the scalar) in
all directions.
the gradient of a function (the vector) always points in the
direction of the maximum rate of change of the function.
the magnitude of the gradient of a function at a point is the
maximum rate of change of the function.
the gradient of a function at a point is normal to the level
curve (or surface, or object, ...) at that point.
14.7 Maximum and Minimum Values
recommended exercises
3, 7, 11, 17, 21, 27, 31, 34, 35, 42, 45, 49, 54
learning objectives
use partial derivatives to locate critical points for a function of two
variables
apply the second derivative test to identify a critical point as a local
maximum, local minimum, or saddle point for a function of two variables
examine critical points and boundary points to find absolute maximum and
minimum values for a function of two variables
additional notes
checking the boundary of a region, where present, for extreme values is
absolutely necessary.
the analyses of the boundary of a region and the interior of a region are
separate processes
the second derivative test can only be used in the interior of a region
14.8 Lagrange Multipliers
recommended exercises
5, 9, 14, 17, 25, 31, 37, 43, 48
learning objectives
use the method of Lagrange multipliers to solve optimization problems with one constraint
use the method of Lagrange multipliers to solve optimization problems with two constraints
additional notes
maximize/minimize with one constraint
the gradients are colinear at the locations of extreme values
the gradient of the objective function is a multiple of the gradient of the constraint function
maximize/minimize with two constraints
the gradients are coplanar at the locations of extreme values
the gradient of the objective function is a linear combination of the gradients of the constraint functions
solving nonlinear equations
following the branches of a tree
semesters >
fall 2024 >
mth261 >
week 7 >
assessments
Assessments
Deadlines
All homework is due at the beginning of class on the date indicated.
All exams are given in class on the date indicated.
Late homework is not accepted and there are no make-ups for missed exams.
Contact me before the deadline if you have a conflict with a
deadline and we will find a mutually agreeable solution.
Solutions are posted under these links after the deadline.
Homework
due Thursday, October 10:
due Thursday, October 17:
due Thursday, October 24:
Exams
Thursday, November 7: Exam 2
Exam 2
(solutions)
Exam 2 covers sections 14.4-14.8 and 15.1-15.7
semesters >
fall 2024 >
mth261 >
week 7 >
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