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A \({\bf{2}} \times {\bf{200}}\) data matrix \(D\) contains the coordinate of 200 points. Compute the number of multiplications required to transform these points using two arbitrary \({\bf{2}} \times {\bf{2}}\) matrices \(A\) and \(B\). Consider the two possibilities \[A\left( {BD} \right)\] and \(\left( {AB} \right)D\). Dicuss the implications of your results for computer graphics calculations.

Short Answer

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1600 multiplications, 808 multiplications

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01

Find the number of multiplications for \(A\left( {BD} \right)\)

For producing each entry in \(BD\), two multiplications are necessary. Since \(BD\) is a \(2 \times 200\) matrix, it will take \(2 \times 2 \times 200 = 800\;{\rm{multiplications}}\) to compute it. By the same reasoning, it will take \(2 \times 2 \times 200 = 800\;{\rm{multiplications}}\) to compute \(A\left( {BD} \right)\). Thus, to compute \(A\left( {BD} \right)\) from the beginning, it will take \(1600\;{\rm{multiplications}}\).

02

Find the number of multiplications for \(\left( {AB} \right)D\)

To compute the \(2 \times 2\) matrix \(AB\), it will take \(2 \times 2 \times 2 = 8\;{\rm{multiplications}}\), and to compute \(\left( {AB} \right)D\), it will take \(2 \times 2 \times 200 = 800\)multiplications. Thus, to compute \(\left( {AB} \right)D\) from the beginning, it will take \(8 + 800 = 808\) multiplications.

03

Implication of the results in computer graphics

For computer graphics, calculations require the application of multiple transformations to data matrices. It is thus more efficient to compute the product of the transformation matrices before applying the result to the data matrix.

So, \(A\left( {BD} \right)\) requires 1600 multiplications, and \(\left( {AB} \right)D\) requires 808 multiplications. The first method uses twice as many applications.

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Most popular questions from this chapter

Solve the equation \(AB = BC\) for A, assuming that A, B, and C are square and Bis invertible.

Use the inverse found in Exercise 3 to solve the system

\(\begin{aligned}{l}{\bf{8}}{{\bf{x}}_{\bf{1}}} + {\bf{5}}{{\bf{x}}_{\bf{2}}} = - {\bf{9}}\\ - {\bf{7}}{{\bf{x}}_{\bf{1}}} - {\bf{5}}{{\bf{x}}_{\bf{2}}} = {\bf{11}}\end{aligned}\)

[M] Suppose memory or size restrictions prevent your matrix program from working with matrices having more than 32 rows and 32 columns, and suppose some project involves \(50 \times 50\) matrices A and B. Describe the commands or operations of your program that accomplish the following tasks.

a. Compute \(A + B\)

b. Compute \(AB\)

c. Solve \(Ax = b\) for some vector b in \({\mathbb{R}^{50}}\), assuming that \(A\) can be partitioned into a \(2 \times 2\) block matrix \(\left[ {{A_{ij}}} \right]\), with \({A_{11}}\) an invertible \(20 \times 20\) matrix, \({A_{22}}\) an invertible \(30 \times 30\) matrix, and \({A_{12}}\) a zero matrix. [Hint: Describe appropriate smaller systems to solve, without using any matrix inverse.]

Exercises 15 and 16 concern arbitrary matrices A, B, and Cfor which the indicated sums and products are defined. Mark each statement True or False. Justify each answer.

15. a. If A and B are \({\bf{2}} \times {\bf{2}}\) with columns \({{\bf{a}}_1},{{\bf{a}}_2}\) and \({{\bf{b}}_1},{{\bf{b}}_2}\) respectively, then \(AB = \left( {\begin{aligned}{*{20}{c}}{{{\bf{a}}_1}{{\bf{b}}_1}}&{{{\bf{a}}_2}{{\bf{b}}_2}}\end{aligned}} \right)\).

b. Each column of ABis a linear combination of the columns of Busing weights from the corresponding column of A.

c. \(AB + AC = A\left( {B + C} \right)\)

d. \({A^T} + {B^T} = {\left( {A + B} \right)^T}\)

e. The transpose of a product of matrices equals the product of their transposes in the same order.

Let \(A = \left( {\begin{aligned}{*{20}{c}}{\bf{1}}&{\bf{2}}\\{\bf{5}}&{{\bf{12}}}\end{aligned}} \right),{b_{\bf{1}}} = \left( {\begin{aligned}{*{20}{c}}{ - {\bf{1}}}\\{\bf{3}}\end{aligned}} \right),{b_{\bf{2}}} = \left( {\begin{aligned}{*{20}{c}}{\bf{1}}\\{ - {\bf{5}}}\end{aligned}} \right),{b_{\bf{3}}} = \left( {\begin{aligned}{*{20}{c}}{\bf{2}}\\{\bf{6}}\end{aligned}} \right),\) and \({b_{\bf{4}}} = \left( {\begin{aligned}{*{20}{c}}{\bf{3}}\\{\bf{5}}\end{aligned}} \right)\).

  1. Find \({A^{ - {\bf{1}}}}\), and use it to solve the four equations \(Ax = {b_{\bf{1}}},\)\(Ax = {b_2},\)\(Ax = {b_{\bf{3}}},\)\(Ax = {b_{\bf{4}}}\)\(\)
  2. The four equations in part (a) can be solved by the same set of row operations, since the coefficient matrix is the same in each case. Solve the four equations in part (a) by row reducing the augmented matrix \(\left( {\begin{aligned}{*{20}{c}}A&{{b_{\bf{1}}}}&{{b_{\bf{2}}}}&{{b_{\bf{3}}}}&{{b_{\bf{4}}}}\end{aligned}} \right)\).
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