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Let \({\bf{x}}\left( t \right)\) and \({\bf{y}}\left( t \right)\) be Bézier curves from Exercise 5, and suppose the combined curve has \({C^2}\)continuity (which includes \({C^1}\) continuity) at \({{\bf{p}}_3}\) . Set \({\mathbf{x}}''\left( 1 \right) = {\mathbf{y}}''\left( 0 \right)\) and show that \({{\bf{p}}_5}\) is completely determined by \({{\bf{p}}_1}\) , \({{\bf{p}}_2}\) , and \({{\bf{p}}_3}\) . Thus, the points \({{\bf{p}}_0},...,\,{{\bf{p}}_3}\)and the \({C^2}\) condition determine all but one of the control points for \({\bf{y}}\left( t \right)\).

Short Answer

Expert verified

It is shown that \({{\bf{p}}_5}\) is dependent on \({{\bf{p}}_1}\),\({{\bf{p}}_2}\)and \({{\bf{p}}_3}\).

Step by step solution

01

Step 1:Describe the given information

It is given that\({\bf{x}}\left( t \right)\)and\({\bf{y}}\left( t \right)\)are the Bézier curves their combined curve has\({C^2}\)continuity including\({C^1}\)continuityat\({{\bf{p}}_3}\).

It has already been shown that \({\mathbf{x}}''(0) = 6\left( {{{\mathbf{p}}_o} - {{\mathbf{p}}_1}} \right) + 6\left( {{{\mathbf{p}}_2} - {{\mathbf{p}}_1}} \right)\), \({\mathbf{x}}''(1) = 6\left( {{{\mathbf{p}}_1} - {{\mathbf{p}}_2}} \right) + 6\left( {{{\mathbf{p}}_3} - {{\mathbf{p}}_2}} \right)\) and if x''(0) is the control point of\({\bf{y}}\left( t \right)\), then \({\mathbf{y}}''(0) = 6\left( {{{\mathbf{p}}_3} - {{\mathbf{p}}_4}} \right) + 6\left( {{{\mathbf{p}}_5} - {{\mathbf{p}}_4}} \right)\) .

02

Step 2:Set \({\mathbf{x}}''\left( 1 \right) = {\mathbf{y}}''\left( 0 \right)\)

Find the relation as shown below:

\(\begin{array}{c}\left( {{{\bf{p}}_1} - {{\bf{p}}_2}} \right) + 6\left( {{{\bf{p}}_3} - {{\bf{p}}_2}} \right) = 6\left( {{{\bf{p}}_3} - {{\bf{p}}_4}} \right) + 6\left( {{{\bf{p}}_5} - {{\bf{p}}_4}} \right)\\\left( {{{\bf{p}}_1} - {{\bf{p}}_2}} \right) + \left( {{{\bf{p}}_3} - {{\bf{p}}_2}} \right) = \left( {{{\bf{p}}_3} - {{\bf{p}}_4}} \right) + \left( {{{\bf{p}}_5} - {{\bf{p}}_4}} \right)\end{array}\)

We can write \({{\bf{p}}_3} = \frac{1}{2}\left( {{{\bf{p}}_2} + {{\bf{p}}_4}} \right)\)or\({{\bf{p}}_4} = {{\bf{p}}_3} + {{\bf{p}}_3} - {{\bf{p}}_2}\) as the curves have \({C^1}\) continuity at \({{\bf{p}}_3}\).

03

Plug in  \({{\bf{p}}_3} = \frac{1}{2}\left( {{{\bf{p}}_2} + {{\bf{p}}_4}} \right)\) into the resulting equation

The equation becomes as shown below:

\(\begin{array}{c}\left( {{{\bf{p}}_1} - {{\bf{p}}_2}} \right) + \left( {\frac{1}{2}\left( {{{\bf{p}}_2} + {{\bf{p}}_4}} \right) - {{\bf{p}}_2}} \right) = \left( {\frac{1}{2}\left( {{{\bf{p}}_2} + {{\bf{p}}_4}} \right) - {{\bf{p}}_4}} \right) + \left( {{{\bf{p}}_5} - {{\bf{p}}_4}} \right)\\\left( {{{\bf{p}}_1} - {{\bf{p}}_2}} \right) + 2\left( {{{\bf{p}}_3} - {{\bf{p}}_2}} \right) + {{\bf{p}}_4} = {{\bf{p}}_5}\end{array}\)

04

Plugin  \({{\bf{p}}_4} = {{\bf{p}}_3} + {{\bf{p}}_3} - {{\bf{p}}_2}\) into the resulting equation 

\(\begin{array}{c}\left( {{{\bf{p}}_1} - {{\bf{p}}_2}} \right) + 2\left( {{{\bf{p}}_3} - {{\bf{p}}_2}} \right) + {{\bf{p}}_3} + {{\bf{p}}_3} - {{\bf{p}}_2} = {{\bf{p}}_5}\\{{\bf{p}}_5} = {{\bf{p}}_3} + \left( {{{\bf{p}}_1} - {{\bf{p}}_2}} \right) + 3\left( {{{\bf{p}}_3} - {{\bf{p}}_2}} \right)\end{array}\)

05

Draw a conclusion

As \({{\bf{p}}_4} = {{\bf{p}}_3} + {{\bf{p}}_3} - {{\bf{p}}_2}\) and \({{\bf{p}}_5} = {{\bf{p}}_3} + \left( {{{\bf{p}}_1} - {{\bf{p}}_2}} \right) + 3\left( {{{\bf{p}}_3} - {{\bf{p}}_2}} \right)\), it is stated that \({{\bf{p}}_4}\)and \({{\bf{p}}_5}\)depends on \({{\bf{p}}_1}\),\({{\bf{p}}_2}\)and \({{\bf{p}}_3}\).

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Most popular questions from this chapter

Determine which of the matrices in Exercises 1–6 are symmetric.

1. \(\left[ {\begin{aligned}{{}}3&{\,\,\,5}\\5&{ - 7}\end{aligned}} \right]\)

In Exercises 3-6, find (a) the maximum value of \(Q\left( {\rm{x}} \right)\) subject to the constraint \({{\rm{x}}^T}{\rm{x}} = 1\), (b) a unit vector \({\rm{u}}\) where this maximum is attained, and (c) the maximum of \(Q\left( {\rm{x}} \right)\) subject to the constraints \({{\rm{x}}^T}{\rm{x}} = 1{\rm{ and }}{{\rm{x}}^T}{\rm{u}} = 0\).

4. \(Q\left( x \right) = 3x_1^2 + 3x_2^2 + 5x_3^2 + 6x_1^{}x_2^{} + 2x_1^{}x_3^{} + 2x_2^{}x_3^{}\).

In Exercises 17–24, \(A\) is an \(m \times n\) matrix with a singular value decomposition \(A = U\Sigma {V^T}\) , where \(U\) is an \(m \times m\) orthogonal matrix, \({\bf{\Sigma }}\) is an \(m \times n\) “diagonal” matrix with \(r\) positive entries and no negative entries, and \(V\) is an \(n \times n\) orthogonal matrix. Justify each answer.

21. Justify the statement in Example 2 that the second singular value of a matrix \(A\) is the maximum of \(\left\| {A{\bf{x}}} \right\|\) as \({\bf{x}}\) varies over all unit vectors orthogonal to \({{\bf{v}}_{\bf{1}}}\), with \({{\bf{v}}_{\bf{1}}}\) a right singular vector corresponding to the first singular value of \(A\). (Hint: Use Theorem 7 in Section 7.3.)

Question: 14. Exercises 12–14 concern an \(m \times n\) matrix \(A\) with a reduced singular value decomposition, \(A = {U_r}D{V_r}^T\), and the pseudoinverse \({A^ + } = {U_r}{D^{ - 1}}{V_r}^T\).

Given any \({\rm{b}}\) in \({\mathbb{R}^m}\), adapt Exercise 13 to show that \({A^ + }{\rm{b}}\) is the least-squares solution of minimum length. [Hint: Consider the equation \(A{\rm{x}} = {\rm{b}}\), where \(\mathop {\rm{b}}\limits^\^ \) is the orthogonal projection of \({\rm{b}}\) onto Col \(A\).

Question: Let \({x_1}\,,{x_2}\) denote the variables for the two-dimensional data in Exercise 1. Find a new variable \({y_1}\) of the form \({y_1} = {c_1}{x_1} + {c_2}{x_2}\), with\(c_1^2 + c_2^2 = 1\), such that \({y_1}\) has maximum possible variance over the given data. How much of the variance in the data is explained by \({y_1}\)?

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