Optimizing Multivariate Function with Constraint: Lagrange Multiplier Troubles?

In summary, the conversation discusses finding extrema for a function under a given constraint using the gradient method. The attempt at a solution involves setting the partial derivatives of the function and the constraint equal to each other and solving for potential extrema. However, there are multiple solutions to consider and further analysis is needed to determine which are maxima, minima, or constrained saddle points.
  • #1
Contingency
41
0

Homework Statement


Find extrema for [itex]f\left( x,y,z \right) ={ x }^{ 3 }+{ y }^{ 3 }+{ z }^{ 3 }[/itex]
under the constraint [itex]g\left( x,y,z \right) ={ x }^{ 2 }+{ y }^{ 2 }+{ z }^{ 2 }=16[/itex]

Homework Equations


(1) [itex]\nabla f=\lambda \nabla g[/itex]
(2) [itex]g\left( x,y,z \right) ={ x }^{ 2 }+{ y }^{ 2 }+{ z }^{ 2 }=16[/itex]

The Attempt at a Solution


(1)[itex]\Rightarrow \left( 3{ x }^{ 2 },3{ y }^{ 2 },3{ z }^{ 2 } \right) =\lambda \left( 2x,2y,2z \right)[/itex]⇔(3) [itex]x=y=z[/itex]
(3)→(2)[itex]\Rightarrow x=y=z=\pm \frac { 4 }{ \sqrt { 3 } }[/itex]
But subbing in x=y=0, z=4 gives a greater value..
What am I doing wrong?
 
Last edited:
Physics news on Phys.org
  • #2
Contingency said:

Homework Statement


Find extrema for [itex]f\left( x,y,z \right) ={ x }^{ 3 }+{ y }^{ 3 }+{ z }^{ 3 }[/itex]
under the constraint [itex]g\left( x,y,z \right) ={ x }^{ 2 }+{ y }^{ 2 }+{ z }^{ 2 }=16[/itex]


Homework Equations


(1) [itex]\nabla f=\lambda \nabla g[/itex]
(2) [itex]g\left( x,y,z \right) ={ x }^{ 2 }+{ y }^{ 2 }+{ z }^{ 2 }=16[/itex]

The Attempt at a Solution


(1)[itex]\Rightarrow \left( 3{ x }^{ 2 },3{ y }^{ 2 },3{ z }^{ 2 } \right) =\lambda \left( 2x,2y,2z \right)[/itex]⇔(3) [itex]x=y=z[/itex]
(3)→(2)[itex]\Rightarrow x=y=z=\pm \frac { 4 }{ \sqrt { 3 } }[/itex]
But subbing in x=y=0, z=4 gives a greater value..
What am I doing wrong?

##f_{x}=\lambda g_{x} \longrightarrow \: 3x^2 = 2\lambda x,## so either ##x = 0## or ##3x = 2 \lambda##. Similarly for y and z. There are many, many candidate solutions, some of which are maxima, some of which are minima and (perhaps) some of which are constrained saddle points. Besides solutions with x = y = z you also have solutions with, for example, x = 0 and y = z ≠ 0.
 
  • #3
All clear, thank you!
 

Related to Optimizing Multivariate Function with Constraint: Lagrange Multiplier Troubles?

What is Lagrange Multiplier Trouble?

Lagrange Multiplier Trouble, also known as the Lagrange Multiplier Method, is a mathematical optimization technique used to find the maximum or minimum value of a function subject to certain constraints. It is named after the mathematician Joseph-Louis Lagrange, who first described it in the late 18th century.

When is Lagrange Multiplier Trouble used?

Lagrange Multiplier Trouble is often used in economics, physics, and engineering, where problems involve maximizing or minimizing a function subject to constraints. It is also commonly used in machine learning and data analysis to find optimal solutions in a given dataset.

What are the main steps in the Lagrange Multiplier Method?

The main steps in the Lagrange Multiplier Method are:

  • Formulating the objective function and constraints
  • Constructing the Lagrangian function by adding the product of the Lagrange multiplier and the constraints to the objective function
  • Setting the partial derivatives of the Lagrangian function to zero
  • Solving the resulting system of equations to find the optimal values of the variables and the Lagrange multiplier

What are the advantages of using Lagrange Multiplier Trouble?

One of the main advantages of using Lagrange Multiplier Trouble is its versatility - it can be applied to a wide range of optimization problems with multiple constraints. It also provides a systematic approach to finding optimal solutions and can handle both equality and inequality constraints.

What are some common challenges when using Lagrange Multiplier Trouble?

One of the main challenges when using Lagrange Multiplier Trouble is determining the appropriate constraints to include in the problem. Additionally, the method can become computationally intensive as the number of variables and constraints increases. It also relies on the assumption of convexity, which may not always hold true in real-world problems.

Similar threads

  • Calculus and Beyond Homework Help
Replies
8
Views
491
  • Calculus and Beyond Homework Help
Replies
2
Views
526
  • Calculus and Beyond Homework Help
Replies
16
Views
2K
  • Calculus and Beyond Homework Help
Replies
1
Views
474
  • Calculus and Beyond Homework Help
Replies
26
Views
1K
  • Calculus and Beyond Homework Help
Replies
2
Views
564
  • Calculus and Beyond Homework Help
Replies
6
Views
876
  • Calculus and Beyond Homework Help
Replies
4
Views
855
  • Calculus and Beyond Homework Help
Replies
6
Views
1K
  • Calculus and Beyond Homework Help
Replies
9
Views
1K
Back
Top