Question

Suppose \(\rho: M \rightarrow M^{\prime}\) is an injective \(R\) -linear map and that \(\left\\{\alpha_{i}\right\\}_{i=1}^{n}\) is a linearly independent family of elements of \(M .\) Show that \(\left\\{\rho\left(\alpha_{i}\right)\right\\}_{i=1}^{n}\) is linearly independent.

Short answer

Question: Prove that if ρ: M → M' is an injective R-linear map and {αi} is a linearly independent family of elements of the module M, then {ρ(αi)} is also linearly independent. Answer: We proved that if ρ: M → M' is an injective R-linear map and {αi} is a linearly independent family of elements of the module M, then {ρ(αi)} is also linearly independent. This was established by assuming the opposite and reaching a contradiction using the injective property of ρ and the definition of linear independence.

Step-by-step solution

Recall definition of linear independence

A family of elements {αi} is linearly independent if the only linear combination of the elements that equals zero is the one in which all coefficients are zero. In other words, for any scalars {ri} from the ring R, if ∑riαi = 0, then ri = 0 for all i.

Define what it means for ρ to be an injective R-linear map

Since ρ is an injective R-linear map, it satisfies the following properties: 1. ρ(α + β) = ρ(α) + ρ(β) for all α, β ∈ M. 2. ρ(rα) = rρ(α) for all r ∈ R and α ∈ M. 3. If ρ(α) = ρ(β), then α = β for all α, β ∈ M.

Assume that {ρ(αi)} is not linearly independent

Let's assume that the family {ρ(αi)} is not linearly independent. That means there exists a nonzero linear combination of the elements that equals zero: ∑siρ(αi) = 0 for some non-zero coefficients si in R.

Apply the R-linear properties of ρ to the linear combination

Using the R-linear properties of ρ, we can rewrite the linear combination as follows: ρ(∑ siαi) = ∑ siρ(αi) = 0 Notice that we used properties 1 and 2 in this step.

Use the injective property of ρ to arrive at a contradiction

Since ρ is injective, we must have ∑siαi = 0 in M. However, this contradicts the linear independence of {αi}, as we assumed that at least one of the coefficients si is non-zero.

Conclude that {ρ(αi)} is linearly independent

Given the contradiction in Step 5, we must conclude that our assumption in Step 3 is false. Therefore, {ρ(αi)} is linearly independent.

Key concepts

Injective R-linear Map

An injective R-linear map is a fundamental concept in linear algebra that often causes confusion for students. To simplify, imagine an injective map as a kind of function that never maps two different inputs to the same output.

Specifically, let \( \rho: M \rightarrow M' \) be an injective R-linear map. This means that \( \rho \) has some special properties:

• If we take two elements from our domain \( M \), let’s call them \( \alpha \) and \( \beta \) and add them together, \( \rho \) will respect this addition when applied to the result. So, \( \rho(\alpha + \beta) = \rho(\alpha) + \rho(\beta) \).
• If we take an element from \( M \) and scale it by a value from \( R \) (the ring, which we’ll dig into next), \( \rho \) will again respect this operation, so \( \rho(r\alpha) = r\rho(\alpha) \).
• Most importantly, since \( \rho \) is injective, if \( \rho(\alpha) = \rho(\beta) \) then it must be the case that \( \alpha = \beta \) — this is what injectivity means in this context.

Using these properties, we can prove various important results in linear algebra, including statements about the linear independence of images under \( \rho \).

Ring R

In mathematics, a ring is an abstraction that generalizes familiar concepts like the integers, rational numbers, or real numbers. A ring \( R \) has to satisfy several properties, including

• There is an addition operation that is associative, commutative, and has an additive identity (commonly called zero).
• There’s also a multiplicative operation that is associative and has a multiplicative identity (often called one), but unlike in a field, elements do not necessarily have multiplicative inverses.
• Distribution of multiplication over addition holds, meaning \( r(s + t) = rs + rt \) and \( (s + t)r = sr + tr \) for all \( r, s, t \) in \( R \).

In linear algebra, when we talk about an R-linear map, we are referring to these operations within the ring \( R \) and their interactions with the map. Understanding the operations within the ring is key to grasping higher-level concepts such as module theory and vector spaces over \( R \).

Linear Combination

A linear combination is somewhat like a recipe. It involves mixing various ingredients - in this case, elements of a vector space or a module - with specific quantities or 'weights'. These weights come from a ring, like \( R \) that we mentioned earlier.

Mathematically, if we have elements \( \alpha_1, \alpha_2, ..., \alpha_n \) in a module, and we want to form a linear combination, we would select weights (or coefficients) \( r_1, r_2, ..., r_n \) from the ring \( R \) and sum them up as \( r_1\alpha_1 + r_2\alpha_2 + ... + r_n\alpha_n \).

The concept of linear independence tells us that if the only way to make this whole mixture add up to zero - the 'zero vector' or 'null element' - is by setting all these coefficients \( r_i \) to zero, then the elements we started with are 'unique' in a sense and we call them linearly independent. This property is central to understanding vector spaces, subspaces, bases, and dimensions in linear algebra.