How Does the Cauchy-Schwarz Inequality Prove a Vector Inequality?

In summary, the Cauchy-Schwarz inequality states that |<u,v>| ≤ ||u|| ||v||. Using this inequality, it can be shown that (a+b/2)2 ≤ a2+b2/2 for given vectors u = [a b] and v = [1 1]. By simplifying both equations and showing that they are equal, it can be proven that the inequality holds true.
  • #1
SeannyBoi71
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0

Homework Statement


Let u = [a b] and v = [1 1]. Use the Cauchy-Schwarz inequality to show that (a+b/2)2 ≤ a2+b2/2. Those vectors are supposed to be in column form.

Homework Equations


|<u,v>| ≤||u|| ||v||,
and the fact that inner product here is defined by dot product (so <u,v> = u[itex]\cdot[/itex]v)


The Attempt at a Solution


|<u,v>| ≤ ||u|| ||v||

|<[a b],[1 1]>| ≤ ||[a b]|| ||[1 1]||

|a+b| ≤ √(a2+b2)√2

and there is where I'm stuck. Any help please?
 
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  • #2
I can't understand... are a, b real numbers ? Any value ?
Take a=b=1 you have [itex](\frac{3}{2})^2 < \frac{3}{2}[/itex]
 
  • #3
It doesn't specify that they are real numbers, but I can only assume they are supposed to be... and I don't think I can take specific numbers. I have to show the general case keeping a and b in there.
 
  • #4
Hey, after going through the question i found the answer (if you still need it/ anypone else needs to see it)

You have |x [dot] y| <= ||x|| [dot] ||y||

so: |<a,b>[dot]<1,1>| <= ||<a,b>|| [dot] ||<1,1>||

simplify to: |a+b| <= sqrt(a^2+b^2)*sqrt(2)

then square both sides to get: (a+b)^2 <= (a^2+b^2)*2
(you can see triangle inequality here)

Next, simplify other equation.

((a+b)/2)^2 <= (a^2 + b^2)/2

simplify to: (a+b)^2/4 <= (a^2 + b^2)/2

multiply both sides by 4: (a+b)^2 <= (a^2+b^2)*2

And you have both equations the same, therefore it holds.

Hope that helps
 

Related to How Does the Cauchy-Schwarz Inequality Prove a Vector Inequality?

What is the Cauchy-Schwarz Inequality?

The Cauchy-Schwarz Inequality, also known as the Cauchy-Bunyakovsky-Schwarz Inequality, is a fundamental inequality in mathematics that relates the inner product of two vectors to their lengths. It states that the absolute value of the inner product of two vectors is less than or equal to the product of their lengths. In mathematical notation, it can be written as |⟨x,y⟩| ≤ ‖x‖‖y‖, where x and y are vectors and ‖x‖ and ‖y‖ represent their lengths.

What is the significance of the Cauchy-Schwarz Inequality?

The Cauchy-Schwarz Inequality is significant because it has many applications in various fields of mathematics, such as linear algebra, calculus, and probability. It is also used in physics, engineering, and computer science. It is an essential tool for proving other mathematical inequalities and theorems. Additionally, it has practical applications in optimization and data analysis.

How is the Cauchy-Schwarz Inequality used in linear algebra?

In linear algebra, the Cauchy-Schwarz Inequality is used to prove the triangle inequality, which states that the length of the sum of two vectors is less than or equal to the sum of their individual lengths. It is also used to define the notion of orthogonality between vectors and to prove the Cauchy-Schwarz-Bunyakovsky Inequality, which is a generalization of the Cauchy-Schwarz Inequality for inner products of more than two vectors.

What is the relationship between the Cauchy-Schwarz Inequality and the dot product?

The dot product is a special case of the inner product and is closely related to the Cauchy-Schwarz Inequality. In fact, the Cauchy-Schwarz Inequality can be used to prove properties of the dot product, such as the commutative and distributive properties. The dot product can also be used to calculate the lengths of vectors and determine if they are orthogonal, making it closely tied to the Cauchy-Schwarz Inequality.

Can the Cauchy-Schwarz Inequality be generalized to higher dimensions?

Yes, the Cauchy-Schwarz Inequality can be generalized to higher dimensions, also known as the Cauchy-Schwarz-Bunyakovsky Inequality. This generalization allows for the inner product of more than two vectors and is used in higher-dimensional spaces such as matrices, tensors, and function spaces. The proof for the generalization follows the same logic as the original Cauchy-Schwarz Inequality, but with more variables and terms involved.

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