Question

Shew that the nofm defined by an mner product satisfies the paratlelogranu taw $$ \|u+v\|^{2}+\|u-v\|^{2}=2\|u\|^{2}+2\|v\|^{2} $$

Short answer

The parallelogram law is proved by expanding the definition of norms, rearranging terms, and substituting back the definitions.

Step-by-step solution

- Expand the left side using the definition of the norm

The norm of a vector is defined by the square root of the inner product of the vector with itself. We want to square the norm, effectively removing the square root. This lets us write the left side as following:\( (u + v, u + v) + (u - v, u - v) \) where (a, b) represents the inner product of vectors a and b.

- Expand the inner products

Next, we can expand each inner product separately. With the property of distributivity, this gives us:\( (u, u) + 2(u, v) + (v, v) + (u, u) - 2(u, v) + (v, v) \) = \(2(u, u) + 2(v, v)\)

- Substitute Norms

In the final step, substitute the inner products with the corresponding norms, which gives:\(2\|u\|^2 + 2\|v\|^2\) .

Key concepts

Inner Product

An inner product is a mathematical concept that helps us to quantify how close or similar two vectors are to each other. It takes two vectors and returns a single number, often denoted as \((a, b)\). This number gives information about the vectors such as their angles or relative orientations.
For instance:

• The inner product of two identical vectors is always non-negative, illustrating the concept of vector direction and length.
• A zero inner product implies orthogonality between two vectors, meaning they are perpendicular.

In many cases, the inner product is akin to the dot product in three-dimensional spaces. However, it generalizes to more complex scenarios, such as vectors in spaces of higher dimensions. It's an essential foundation for understanding vector norms and operations like the parallelogram law.

Vector Norms

Vector norms provide a way to measure the length or size of a vector. It’s a critical tool in understanding the geometry of vector spaces. In mathematical terms, the norm \(\|u\|\) of a vector \(u\) is derived from the inner product, defined as \(\sqrt{(u, u)}\). This makes the norm always a non-negative value.
The key points about vector norms include:

• It's non-negative, meaning \(\|u\| \geq 0\) for any vector \(u\).
• Only the zero vector has a norm of zero, emphasizing that it has no length or directionality.
• It obeys the triangle inequality, which states that \(\|u + v\| \leq \|u\| + \|v\|\).

These properties make vector norms essential in various applications, from pure mathematics to physics and engineering. Understanding norms is crucial to utilizing the parallelogram law effectively.

Distributive Property

The distributive property is one of the algebraic properties that makes operations with inner products functional. This property indicates that the inner product is linear concerning vector addition. For inner products, the distributive property can be expressed as:

• \((u + v, w) = (u, w) + (v, w)\)

It lays down a fundamental rule for maneuvering expressions that allow complex vector equations to be simplified. Applying this property was vital in expanding the inner products in the given exercise.
During the distribution process, each term is multiplied individually and summed up to provide a neat simplification, ultimately assisting in proving the parallelogram law.

Hilbert Space

A Hilbert space is a conceptual environment in mathematics that extends the idea of Euclidean space to potentially infinite dimensions. It's a framework where both finite and infinite-dimensional vector spaces harbor the rich structure afforded by an inner product.
Some features include:

• Hilbert spaces maintain completeness, meaning every Cauchy sequence of vectors has a limit within the space.
• The inner product defines the geometry and allows for concepts like length and angle to be generalized.

These properties make Hilbert spaces a backbone in functional analysis, quantum mechanics, and other advanced scientific fields. The relevance of Hilbert spaces to the parallelogram law is tied to their inner product structure, as it enables the generalization of concepts like norms and distances in complex vector spaces.