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Prove Theorem \(3.15 .\) Let \(v_{0}\) be a particular solution of \(A X=B,\) and let \(W\) be the general solution of \(A X=0 .\) Then \(U=v_{0}+W=\left\\{v_{0}+w: w \in W\right\\}\) is the general solution of \(A X=B\) Let \(w\) be a solution of \(A X=0 .\) Then

Short Answer

Expert verified
We have demonstrated that an arbitrary element u in set U is a solution of AX = B and that any solution of AX = B can be expressed in the form of an element in set U. Thus, the theorem stating that the general solution of AX = B is given by \(U = v_0 + W\) has been proven.

Step by step solution

01

Show that any element in U is a solution of AX = B

Let u be an arbitrary element in U. By definition, we have: u = vâ‚€ + w, where w is an element in W. As w is in W, it is a solution to AX = 0: A * w = 0. Also, we are given that vâ‚€ is a solution to AX = B: A * vâ‚€ = B. Now let's consider A * u: A * u = A * (vâ‚€ + w) = A * vâ‚€ + A * w. Since A * vâ‚€ = B and A * w = 0, we have: A * u = B + 0 = B. This shows that any element in U is a solution of AX = B.
02

Show that any solution of AX = B can be written in the form of an element in U

Let x be a solution of AX = B, so we have: A * x = B. We also know that A * v₀ = B, so we can write: A * x = A * v₀. Now, consider the vector difference y = x - v₀. We can rewrite the equation as: A * (x - v₀) = A * y. A * y = A * x - A * v₀ = B - B = 0. This means that y is a solution of the homogeneous system (AY = 0), and thus, y belongs to W. Now we can rewrite x in terms of v₀ and y: x = v₀ + y. Since y belongs to W, we have: x = v₀ + w, where w = y ∈ W. This shows that any solution of AX = B can be written in the form of an element in U.
03

Conclusion

We have shown that: 1. Any element in U is a solution of AX = B. 2. Any solution of AX = B can be written in the form of an element in U. Therefore, U = vâ‚€ + W is the general solution of AX = B, proving the theorem.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Particular Solution
In linear algebra, a particular solution refers to a specific solution to a matrix equation of the form \(AX = B\). Here, \(A\) is a given matrix, \(X\) is the vector of unknowns, and \(B\) is the result vector. A particular solution, such as \(v_0\), satisfies the equation \(AX = B\) uniquely, but it is not the only solution possible for this equation.

Think of it as a specific set of values for \(X\) where the condition holds true. Finding a particular solution is crucial because it helps establish a baseline from which we can explore other solutions, particularly in relation to homogeneous systems and general solutions. By identifying \(v_0\), we have a concrete starting point to build further solutions or combinations.
Homogeneous System
A homogeneous system is a fundamental concept in linear algebra involving equations of the form \(AX = 0\). This means we have a system where the result vector is zero. The solutions to these kinds of equations are vectors that, when multiplied by matrix \(A\), result in a zero vector.

The set of all solutions to this equation is called the null space of \(A\), represented by \(W\) in our context. Each vector within this null space is a solution to the homogeneous system. This concept is significant because it helps to determine the full set of solutions to non-homogeneous systems like \(AX = B\). By understanding the structure of \(W\), we gain insights into the solution possibilities of related equations.
General Solution
The general solution of a matrix equation like \(AX = B\) integrates both particular and homogeneous components. It combines a particular solution \(v_0\) with the set of all solutions of the corresponding homogeneous system \(AX = 0\), denoted \(W\).

Formally, the general solution can be expressed as \(U = v_0 + W\), where each solution \(u\) in \(U\) is formed by adding \(v_0\) to any vector \(w\) in \(W\). This means that the general solution not only solves \(AX = B\) but also represents all potential solutions due to the infinite possibilities of combinations with the null space \(W\). Understanding this concept provides a comprehensive picture of how solutions to matrix equations are structured and interrelated.
Matrix Equations
Matrix equations are a central theme in linear algebra, representing relationships between vectors and matrices. A typical matrix equation takes the form \(AX = B\), where \(A\) is a matrix, \(X\) is the unknown vector, and \(B\) is a known vector.

These equations allow us to model and solve linear systems, and they are pivotal in fields ranging from engineering to computer science. A matrix equation can represent numerous variables and constraints succinctly. Solving these equations often involves finding particular solutions, analyzing associated homogeneous systems, and deriving the general solution encompassing all possible answers.

Matrix equations help unravel complex systems by providing a structured mathematical framework, making them vital tools for problem-solving in various applications.

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Most popular questions from this chapter

Prove Theorem \(3.19: B\) is row equivalent to \(A\) (written \(B \sim A\) ) if and only if there exists a nonsingular matrix \(P\) such that \(B=P A\)

Solve each of the following systems: \(\begin{array}{rr}x_{1}-3 x_{2}+2 x_{3}-x_{4}+2 x_{5}=2 & x_{1}+2 x_{2}-3 x_{3}+4 x_{4}=2 \\ 3 x_{1}-9 x_{2}+7 x_{3}-x_{4}+3 x_{5}=7 & 2 x_{1}+5 x_{2}-2 x_{3}+x_{4}=1 \\ 2 x_{1}-6 x_{2}+7 x_{3}+4 x_{4}-5 x_{5}=7 & 5 x_{1}+12 x_{2}-7 x_{3}+6 x_{4}=3 \\ \text { (a) } & \text { (b) }\end{array}\) Reduce each system to echelon form using Gaussian elimination: (a) Apply "Replace \(L_{2}\) by \(-3 L_{1}+L_{2}^{n}\) and "Replace \(L_{3}\) by \(-2 L_{1}+L_{3}^{n}\) to eliminate \(x\) from the second and third equations. This yields $$ \begin{aligned} x_{1}-3 x_{2}+2 x_{3}-x_{4}+2 x_{5} &=2 \\ x_{3}+2 x_{4}-3 x_{5} &=1 \\ 3 x_{3}+6 x_{4}-9 x_{5} &=3 \end{aligned} \quad \text { or } \quad x_{1}-3 x_{2}+2 x_{3}-x_{4}+2 x_{5}=2 } \\\ {x_{3}+2 x_{4}-3 x_{5}=1} \end{array} $$ (We delete \(L_{3}\), because it is a multiple of \(L_{2} .\) ) The system is in echelon form with pivot variables \(x_{1}\) and \(x_{3}\) and free variables \(x_{2}, x_{4}, x_{5}\) To find the parametric form of the general solution, set \(x_{2}=a, x_{4}=b, x_{5}=c\), where \(a, b, c\) are parameters. Back-substitution yields \(x_{3}=1-2 b+3 c\) and \(x_{1}=3 a+5 b-8 c .\) The general solution is $$ x_{1}=3 a+5 b-8 c, x_{2}=a, x_{3}=1-2 b+3 c, x_{4}=b, x_{5}=c $$ or, equivalently, \(u=(3 a+5 b-8 c, a, 1-2 b+3 c, b, c)\) (b) Eliminate \(x_{1}\) from the second and third equations by the operations "Replace \(L_{2}\) by \(-2 L_{1}+L_{2} "\) and "Replace \(L_{3}\) by \(-5 L_{1}+L_{3} . "\) This yields the system $$ \begin{array}{r} x_{1}+2 x_{2}-3 x_{3}+4 x_{4}=2 \\ x_{2}+4 x_{3}-7 x_{4}=-3 \\ 2 x_{2}+8 x_{3}-14 x_{4}=-7 \end{array} $$ The operation "Replace \(L_{3}\) by \(-2 L_{2}+L_{3} "\) yields the degenerate equation \(0=-1 .\) Thus, the system has no solution (even though the system has more unknowns than equations).

Solve using the condensed format: $$ \begin{array}{r} 2 y+3 z=3 \\ x+y+z=4 \\ 4 x+8 y-3 z=35 \end{array} $$ The condensed format follows: \(\begin{array}{lll}\text { Number } & \text { Equation } & \text { Operation } \\\ (2) & (\boldsymbol{1}) & 2 y+3 z=3 & L_{1} \leftrightarrow L_{2} \\ (1) & (2) & x+y+z=4 & L_{1} \leftrightarrow L_{2} \\ (3) & 4 x+8 y-3 z=35 & \\\ \left(3^{\prime}\right) & 4 y-7 z=19 & \text { Replace } L_{3} \text { by }-4 L_{1}+L_{3} \\ \left(3^{\prime \prime}\right) & -13 z=13 & \text { Replace } L_{3} \text { by }-2 L_{2}+L_{3}\end{array}\) Here \((1),(2)\), and \(\left(3^{\prime \prime}\right)\) form a triangular system. (We emphasize that the interchange of \(L_{1}\) and \(L_{2}\) is accomplished by simply renumbering \(L_{1}\) and \(L_{2}\) as above.) Using back-substitution with the triangular system yields \(z=-1\) from \(L_{3}, y=3\) from \(L_{2}\), and \(x=2\) from \(L_{1} .\) Thus, the unique solution of the system is \(x=2, y=3, z=-1\) or the triple \(u=(2,3,-1)\).

Find the dimension and a basis for the general solution \(W\) of the following homogeneous system using matrix notation: $$ \begin{array}{r} x_{1}+2 x_{2}+3 x_{3}-2 x_{4}+4 x_{5}=0 \\ 2 x_{1}+4 x_{2}+8 x_{3}+x_{4}+9 x_{5}=0 \\ 3 x_{1}+6 x_{2}+13 x_{3}+4 x_{4}+14 x_{5}=0 \end{array} $$ Show how the basis gives the parametric form of the general solution of the system. When a system is homogeneous, we represent the system by its coefficient matrix \(A\) rather than by its augmented matrix \(M\), because the last column of the augmented matrix \(M\) is a zero column, and it will remain a zero column during any row-reduction process. Reduce the coefficient matrix \(A\) to echelon form, obtaining $$ A=\left[\begin{array}{rrrrr} 1 & 2 & 3 & -2 & 4 \\ 2 & 4 & 8 & 1 & 9 \\ 3 & 6 & 13 & 4 & 14 \end{array}\right] \sim\left[\begin{array}{rrrrr} 1 & 2 & 3 & -2 & 4 \\ 0 & 0 & 2 & 5 & 1 \\ 0 & 0 & 4 & 10 & 2 \end{array}\right] \sim\left[\begin{array}{rrrrr} 1 & 2 & 3 & -2 & 4 \\ 0 & 0 & 2 & 5 & 1 \end{array}\right] $$ (The third row of the second matrix is deleted, because it is a multiple of the second row and will result in a zero row.) We can now proceed in one of two ways. (a) Write down the corresponding homogeneous system in echelon form: $$ \begin{array}{r} x_{1}+2 x_{2}+3 x_{3}-2 x_{4}+4 x_{5}=0 \\ 2 x_{3}+5 x_{4}+x_{5}=0 \end{array} $$ The system in echelon form has three free variables, \(x_{2}, x_{4}, x_{5}\), so \(\operatorname{dim} W=3\). A basis \(\left[u_{1}, u_{2}, u_{3}\right]\) for \(W\) may be obtained as follows: (1) Set \(x_{2}=1, x_{4}=0, x_{5}=0\). Back-substitution yields \(x_{3}=0\), and then \(x_{1}=-2\). Thus, \(u_{1}=(-2,1,0,0,0)\) (2) Set \(x_{2}=0, x_{4}=1, x_{5}=0\). Back-substitution yields \(x_{3}=-\frac{5}{2}\), and then \(x_{1}=\frac{19}{2}\). Thus, \(u_{2}=\left(\frac{19}{2}, 0,-\frac{5}{2}, 1,0\right)\) (3) Set \(x_{2}=0, x_{4}=0, x_{5}=1\). Back-substitution yields \(x_{3}=-\frac{1}{2}\), and then \(x_{1}=-\frac{5}{2}\). Thus, \(u_{3}=\left(-\frac{5}{2}, 0,-\frac{1}{2}, 0,1\right)\) [One could avoid fractions in the basis by choosing \(x_{4}=2\) in (2) and \(x_{5}=2\) in (3), which yields multiples of \(u_{2}\) and \(u_{3}\).] The parametric form of the general solution is obtained from the following linear combination of the basis vectors using parameters \(a, b, c\) : $$ a u_{1}+b u_{2}+c u_{3}=\left(-2 a+\frac{19}{2} b-\frac{5}{2} c, a,-\frac{5}{2} b-\frac{1}{2} c, b, c\right) $$ (b) Reduce the echelon form of \(A\) to row canonical form: $$ A \sim\left[\begin{array}{ccccc} 1 & 2 & 3 & -2 & 4 \\ 0 & 0 & 1 & \frac{5}{2} & \frac{1}{2} \end{array}\right] \sim\left[\begin{array}{rrrrr} 1 & 2 & 3 & -\frac{19}{2} & \frac{5}{2} \\ 0 & 0 & 1 & \frac{5}{2} & \frac{1}{2} \end{array}\right] $$ Write down the corresponding free-variable solution: $$ \begin{aligned} &x_{1}=-2 x_{2}+\frac{19}{2} x_{4}-\frac{5}{2} x_{5} \\ &x_{3}=-\frac{5}{2} x_{4}-\frac{1}{2} x_{5} \end{aligned} $$ Using these equations for the pivot variables \(x_{1}\) and \(x_{3}\), repeat the above process to obtain a basis \(\left[u_{1}, u_{2}, u_{3}\right]\) for \(W\). That is, set \(x_{2}=1, x_{4}=0, x_{5}=0\) to get \(u_{1} ;\) set \(x_{2}=0, x_{4}=1, x_{5}=0\) to get \(u_{2}\); and set \(x_{2}=0\), \(x_{4}=0, x_{5}=1\) to get \(u_{3}\).

Describe the pivoting row-reduction algorithm. Also describe the advantages, if any, of using this pivoting algorithm.

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