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Vector Algebra

April 20, 2019 by phani

Vector

A Vector may be described as a quantity having both magnitude & direction. A vector is generally represented by a directed line segment, say \(\overrightarrow {AB}\).  A is called the initial point & B is called the terminal point. The magnitude of vector \(\overrightarrow {AB}\) is expressed b \(|\overrightarrow {AB}|\)

Zero Vector 
 a vector of zero magnitude i.e.which has the same initial & terminal point, is called a Zero Vector. It is denoted by O. Unit Vector a vector of unit magnitude in direction of a vector \(\overrightarrow{\mathrm{a}}\) is called unit vector along \(\overrightarrow{\mathrm{a}}\) and is denoted by \(\hat{\mathfrak{a}}\) symbolically \(\hat{\mathfrak{a}}=\frac{\overrightarrow{\mathrm{a}}}{|\overrightarrow{\mathrm{a}}|}.\)

Equal Vectors 
 two vectors are said to be equal if they have the same magnitude, direction & represent the same physical quantity.

Collinear Vectors
  two vectors are said to be collinear if their directed line segments are parallel disregards to their direction. Collinear vectors are also called Parallel Vectors. If they have the same direction they are named as like vectors otherwise unlike vectors. Symbolically, two non zero vectors \(\overrightarrow{\mathrm{a}} \text { and } \overrightarrow{\mathrm{b}}\) are collinear if and only if, \(\overrightarrow{\mathrm{a}}=\mathrm{K} \overrightarrow{\mathrm{b}}\), where K ∈ R Collinear Vectors a given number of vectors are called coplanar if their line segments are all parallel to the same plane. Note that “Two Vectors Are Always Coplanar”. Position Vector let O be a fixed origin, then the position vector of a point P is the vector \(\overrightarrow{\mathrm{OP}}.\)  If \(\overrightarrow{\mathrm{a}\quad}\& \overrightarrow{\quad \mathrm{b}}\)  & position vectors of two point A and B, then, \(\overrightarrow{\mathrm{AB}}=\overrightarrow{\mathrm{b}}-\overrightarrow{\mathrm{a}}=\mathrm{pv} \text { of } \mathrm{B}-\mathrm{pv} \text { of } \mathrm{A}\)

Vector Addition | Vector Algebra

– If two vectors \(\overrightarrow{\mathbf{a}} \boldsymbol{\boldsymbol { X }} \overrightarrow{\mathbf{b}}\)  are represented by \(\overrightarrow{O A} \& \overrightarrow{O B}\) , then their sum \(\vec{a}+\vec{b}\)  is a vector represented by \(\overrightarrow{\mathrm{OC}}\) where OC is the diagonal of the parallelogram OACB.
– \(\vec{a}+\vec{b}=\vec{b}+\vec{a}\)  (commutative)
– \((\vec{a}+\vec{b})+\vec{c}=\vec{a}+(\vec{b}+\vec{c})\)  (associativity)
–\(\overrightarrow{\mathrm{a}}+\overrightarrow{\mathrm{O}}=\overrightarrow{\mathrm{a}}=\overrightarrow{\mathrm{O}}+\overrightarrow{\mathrm{a}}\)
– \(\overrightarrow{\mathbf{a}}+(-\overrightarrow{\mathbf{a}})=\overrightarrow{\mathbf{O}}=(-\overrightarrow{\mathbf{a}})+\overrightarrow{\mathbf{a}}\)

Multiplication of Vector By Scalars | Vector Algebra

If \(\overrightarrow{\mathrm{a}}\) is a vector & m is a scalar, then \(\mathrm{m} \overrightarrow{\mathrm{a}}\) is a vector parallel to \(\overrightarrow{\mathrm{a}}\) whose
modulus is |m| times that of \(\overrightarrow{\mathrm{a}}.\) This multiplication is called Scalar Multiplication. If \(\overrightarrow{\mathrm{a}} \& \overrightarrow{\mathrm{b}}\) are vectors & m, n are scalars, then:
\(\begin{array}{l}{\mathrm{m}(\overrightarrow{\mathrm{a}})=(\overrightarrow{\mathrm{a}}) \mathrm{m}=\mathrm{ma}} \\ {\mathrm{m}(\mathrm{n} \overrightarrow{\mathrm{a}})=\mathrm{n}(\mathrm{m} \overrightarrow{\mathrm{a}})=(\mathrm{mn}) \overrightarrow{\mathrm{a}}} \\ {(\mathrm{m}+\mathrm{n}) \overrightarrow{\mathrm{a}}=\mathrm{m} \overrightarrow{\mathrm{a}}+\mathrm{n} \overrightarrow{\mathrm{a}}}\end{array}\) \(m(\vec{a}+\vec{b})=m \vec{a}+m \vec{b}\)
Vector Algebra Formulas

Section Formula | Vector Algebra

If \(\overrightarrow{\mathrm{a}} \quad \& \overrightarrow{\mathrm{b}}\) are the position vectors of two points A & B then the p.v. of a point which divides AB in the ratio m : n is given by:
\(\vec{r}=\frac{n \vec{a}+m \vec{b}}{m+n}.\) Note p.v.
of mid point of AB \(=\frac{\vec{a}+\vec{b}}{2}\)

Direction Cosines | Vector Algebra

Let \(\vec{a}=a_{1} \hat{i}+a_{2} \hat{j}+a_{3} \hat{k}\) the angles which this vector makes with the +ve directions OX,OY & OZ are called Direction Angles & their cosines are called the Direction Cosines \(\cos \alpha=\frac{a_{1}}{|\vec{a}|} \quad, \quad \cos \beta=\frac{a_{2}}{|\vec{a}|},\cos \Gamma=\frac{a_{3}}{|\vec{a}|}.\)
Note that, cos² α + cos² β + cos² Γ = 1

Vector Equation of A Line | Vector Algebra

Parametric vector equation of a line passing through two point \(\mathrm{A}(\overrightarrow{\mathrm{a}}) \& \mathrm{B}(\overrightarrow{\mathrm{b}})\) is given by, \(\overrightarrow{\mathbf{r}}=\overrightarrow{\mathbf{a}}+t(\vec{b}-\overrightarrow{\mathbf{a}})\) where t is a parameter. If the line passes through the point \(\mathrm{A}(\overrightarrow{\mathrm{a}})\)  & is parallel to the vector \(\overrightarrow{\mathrm{b}}\) then its equation is, \(\overrightarrow{\mathrm{r}}=\overrightarrow{\mathrm{a}}+\mathrm{t} \overrightarrow{\mathrm{b}}\) Note that the equations of the bisectors of the angles between the lines \(\overrightarrow{\mathrm{r}}=\overrightarrow{\mathrm{a}}+\lambda \overrightarrow{\mathrm{b}}\quad \& \quad \overrightarrow{\mathrm{r}}=\overrightarrow{\mathrm{a}}+\mu \overrightarrow{\mathrm{c}}\) is:
\(\overrightarrow{\mathrm{r}}=\overrightarrow{\mathrm{a}}+\mathrm{t}(\hat{\mathrm{b}}+\hat{\mathrm{c}}) \quad \& \quad \overrightarrow{\mathrm{r}}=\overrightarrow{\mathrm{a}}+\mathrm{p}(\hat{\mathrm{c}}-\hat{\mathrm{b}})\)

Test of Collinearity

Three points A,B,C with position vectors \(\vec{a}, \vec{b}, \vec{c}\) respectively are collinear, if & only if there exist scalars x, y , z not all zero simultaneously such that;
\(x \vec{a}+y \vec{b}+z \vec{c}=0\) where x + y + z = 0.

Scalar Product Of Two Vectors

– \(\overrightarrow{\mathrm{a}} \cdot \overrightarrow{\mathrm{b}}=|\overrightarrow{\mathrm{a}}||\overrightarrow{\mathrm{b}}| \cos \theta(0 \leq \theta \leq \pi),\) note that if θ is acute then \(\overrightarrow{\mathrm{a}} \overrightarrow{\mathrm{b}}>0\) & if θ is obtuse then \(\overrightarrow{\mathrm{a}} \overrightarrow{\mathrm{b}}<0\)
– \(\overrightarrow{\mathrm{a}} \overrightarrow{\mathrm{a}}=|\overrightarrow{\mathrm{a}}|^{2}=\overrightarrow{\mathrm{a}}^{2}, \overrightarrow{\mathrm{a}} \overrightarrow{\mathrm{b}}=\overrightarrow{\mathrm{b}} \overrightarrow{\mathrm{a}}\) (commutative)
– \(\vec{a} \cdot(\vec{b}+\vec{c})=\vec{a} \cdot \vec{b}+\vec{a} \cdot \vec{c}\) (distributive)
– \(\overrightarrow{\mathrm{a}} \cdot \overrightarrow{\mathrm{b}}=0 \Leftrightarrow \overrightarrow{\mathrm{a}} \perp \overrightarrow{\mathrm{b}} \quad(\overrightarrow{\mathrm{a}} \neq 0 \quad \overrightarrow{\mathrm{b}} \neq 0)\)
– \(\hat{\mathrm{i}} . \hat{\mathrm{i}}=\hat{\mathrm{j}} \cdot \hat{\mathrm{j}}=\hat{\mathrm{k}} \cdot \hat{\mathrm{k}}=1 ; \hat{\mathrm{i}} \cdot \hat{\mathrm{j}}=\hat{\mathrm{j}} \hat{\mathrm{k}}=\hat{\mathrm{k}} \cdot \hat{\mathrm{i}}=0\)
– projection of \(\overrightarrow{\mathrm{a}} \text { on } \overrightarrow{\mathrm{b}}=\frac{\overrightarrow{\mathrm{a}} \cdot \overrightarrow{\mathrm{b}}}{|\overrightarrow{\mathrm{b}}|}\)
Note: That vector component of \(\vec{a} \text { along } \vec{b}=\left(\frac{\vec{a} \cdot \vec{b}}{\vec{b}^{2}}\right) \vec{b}\) and perpendicular to \(\overrightarrow{\mathrm{b}}=\overrightarrow{\mathrm{a}}-\left(\frac{\overrightarrow{\mathrm{a}} \cdot \overrightarrow{\mathrm{b}}}{\overrightarrow{\mathrm{b}}^{2}}\right) \overrightarrow{\mathrm{b}}.\)
–  the angle φ between \(\overrightarrow{\mathrm{a}} \quad \&\quad \overrightarrow{\mathrm{b}}\) is given by
\(\cos \phi=\frac{\overrightarrow{\mathrm{a}} \cdot \overrightarrow{\mathrm{b}}}{|\overrightarrow{\mathrm{a}}| \overrightarrow{\mathrm{b}} |}\)  0 ≤ φ ≤ π
– if \(\vec{a}=a_{1} \hat{i}+a_{2} \hat{j}+a_{3} \hat{k} \quad \& \quad \vec{b}=b_{1} \hat{i}+b_{2} \hat{j}+b_{3} \hat{k} \text { then } \vec{a} \vec{b}=a_{1} b_{1}+a_{2} b_{2}+a_{3} b_{3}\)
\(|\overrightarrow{\mathrm{a}}|=\sqrt{\mathrm{a}_{1}^{2}+\mathrm{a}_{2}^{2}+\mathrm{a}_{3}^{2}},\)\(|\overrightarrow{\mathrm{b}}|=\sqrt{\mathrm{b}_{1}^{2}+\mathrm{b}_{2}^{2}+\mathrm{b}_{3}^{2}}\)
Note:
(i) Maximum value of \(\overrightarrow{\mathrm{a}} \cdot \overrightarrow{\mathrm{b}}=|\overrightarrow{\mathrm{a}}||\overrightarrow{\mathrm{b}}|\)
(ii) Minimum values of \(\vec{a} \cdot \vec{b}=\vec{a} \cdot \vec{b}=-|\vec{a}||\vec{b}|\)
(iii) Any vector \(\overrightarrow{\mathrm{a}}\) can be written as,\(\vec{a}=(\vec{a} \cdot \hat{i}) \hat{i}+(\vec{a} \cdot \hat{j}) \hat{j}+(\vec{a} \cdot \hat{k}) \hat{k}\)
(iv) A vector in the direction of the bisector of the angle between the two vectors \(\overrightarrow{\mathrm{a}} \& \overrightarrow{\mathrm{b}} \text { is } \frac{\overrightarrow{\mathrm{a}}}{|\overrightarrow{\mathrm{a}}|}+\frac{\overrightarrow{\mathrm{b}}}{|\overrightarrow{\mathrm{b}}|}.\) Hence bisector of the angle between the two vectors \(\overrightarrow{\mathrm{a}} \& \overrightarrow{\mathrm{b}} \text { is } \lambda(\hat{\mathrm{a}}+\hat{\mathrm{b}}),\) where λ ∈ R+. Bisector of the exterior angle between \(\vec{a} \& \vec{b} \text { is } \lambda(\hat{a}-\hat{b}), \lambda \in R^{+}.\)

Vector Product Of Two Vectors

  • If \(\overrightarrow{\mathrm{a}} \& \overrightarrow{\mathrm{b}}\) are two vectors & θ is the angle between them then [latexll]\overrightarrow{\mathbf{a}} \times \overrightarrow{\mathbf{b}}=|\overrightarrow{\mathbf{a}}| \vec{b} | \sin \theta \overrightarrow{\mathbf{n}}[/latex] where \(\overrightarrow{\mathrm{n}}\) is the unit vector perpendicular to both \(\vec{a} \& \vec{b}\) such that [latexl]\vec{a}, \vec{b} \& \vec{n}l[/latex] forms a right handed screw system.
  • Lagranges Identity : for any two vectors
    \(\vec{a} \& \vec{b} ;(\vec{a} \times \vec{b})^{2}=\left.|\vec{a}|\right|^{2}|\vec{b}|^{2}-(\vec{a} \cdot \vec{b})^{2}=\left| \begin{array}{ll}{\vec{a} \cdot \vec{a}} & {\vec{a} . \vec{b}} \\ {\overrightarrow{\mathbf{a}} \cdot \vec{b}} & {\vec{b} . \vec{b}}\end{array}\right|\)
  • Formulation of vector product in terms of scalar product: The vector product \(\overrightarrow{\mathbf{a}} \times \overrightarrow{\mathbf{b}}\) is the vector \(\overrightarrow{\mathrm{c}}\) , such that
    (i) \(|\vec{c}|=\sqrt{\vec{a}^{2} \vec{b}^{2}-(\vec{a} \cdot \vec{b})^{2}}\)
    (ii) \(\overrightarrow{\mathrm{c}} \cdot \overrightarrow{\mathrm{a}}=0 ; \quad \overrightarrow{\mathrm{c}} \cdot \overrightarrow{\mathrm{b}}=0\)  and
    (iii) \(\overrightarrow{\mathrm{a}}, \overrightarrow{\mathrm{b}}, \overrightarrow{\mathrm{c}}\) form a right handed system
  • \(\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}}=0 \Leftrightarrow \overrightarrow{\mathrm{a}} \& \overrightarrow{\mathrm{b}}\) are parallel (collinear) \((\vec{a} \neq \mathbf{O}, \overrightarrow{\mathbf{b}} \neq \mathbf{O}) \text { i.e. } \overrightarrow{\mathbf{a}}=\mathbf{K} \overrightarrow{\mathbf{b}}\) , where K is a scalar.
    – \(\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}} \neq \overrightarrow{\mathrm{b}} \times \overrightarrow{\mathrm{a}}\) (not commutative)
    – \((\mathrm{m} \overrightarrow{\mathrm{a}}) \times \overrightarrow{\mathrm{b}}=\overrightarrow{\mathrm{a}} \times(\mathrm{m} \overrightarrow{\mathrm{b}})=\mathrm{m}(\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}})\) where m is a scalar.
    – \(\overrightarrow{\mathbf{a}} \times(\vec{b}+\vec{c})=(\vec{a} \times \vec{b})+(\vec{a} \times \vec{c})\) (distributive)
    – \(\hat{\mathrm{i}} \times \hat{\mathrm{i}}=\hat{\mathrm{j}} \times \hat{\mathrm{j}}=\hat{\mathrm{k}} \times \hat{\mathrm{k}}=0\)
    – \(\hat{\mathrm{i}} \times \hat{\mathrm{j}}=\hat{\mathrm{k}}, \hat{\mathrm{j}} \times \hat{\mathrm{k}}=\hat{\mathrm{i}}, \hat{\mathrm{k}} \times \hat{\mathrm{i}}=\hat{\mathrm{j}}\)
  • If
    \(\overrightarrow{\mathrm{a}}=\mathrm{a}_{1} \hat{\mathrm{i}}+\mathrm{a}_{2} \hat{\mathrm{j}}+\mathrm{a}_{3} \hat{\mathrm{d}} \quad \& \quad \overrightarrow{\mathrm{b}}=\mathrm{b}_{1} \hat{\mathrm{i}}+\mathrm{b}_{2} \hat{\mathrm{j}}+\mathrm{b}_{3} \hat{\mathrm{k}}\text {then}\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}}=\left| \begin{array}{ccc}{\hat{\mathbf{i}}} & {\hat{\mathbf{j}}} & {\hat{\mathbf{k}}} \\ {\mathrm{a}_{1}} & {\mathrm{a}_{2}} & {\mathrm{a}_{3}} \\ {\mathrm{b}_{1}} & {\mathrm{b}_{2}} & {\mathrm{b}_{3}}\end{array}\right|\)
  • Geometrically \(|\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}}|\) = area of the parallelogram whose two adjacent sides are represented by \(\overrightarrow{\mathbf{a}} \& \overrightarrow{\mathbf{b}}\)
  • Unit vector perpendicular to the plane of \(\overrightarrow{\mathrm{a}} \& \overrightarrow{\mathrm{b}} \text { is } \hat{\mathrm{n}}=\pm \frac{\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}}}{|\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}}|}\)
    –  A vector of magnitude ‘r’ & perpendicular to the palne of \(\overrightarrow{\mathrm{a}} \& \overrightarrow{\mathrm{b}} \text { is } \pm \frac{\mathrm{r}(\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}})}{|\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}}|}\)
    –  If θ is the angle between \(\overrightarrow{\mathrm{a}} \& \overrightarrow{\mathrm{b}} \text { then sin } \theta=\frac{|\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}}|}{|\overrightarrow{\mathrm{a}}||\overrightarrow{\mathrm{b}}|}\)
  • Vector area
    –  If \(\vec{a}, \vec{b} \& \vec{c}\) are the pv’s of 3 points A, B & C then the vector area of triangle ABC
    \(=\frac{1}{2}[\overrightarrow{\mathrm{a}} \mathrm{x} \overrightarrow{\mathrm{b}}+\overrightarrow{\mathrm{b}} \mathrm{x} \overrightarrow{\mathrm{c}}+\overrightarrow{\mathrm{c}} \mathrm{x} \overrightarrow{\mathrm{a}}].\) The points A, B & C are collinear if \(\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}}+\overrightarrow{\mathrm{b}} \mathrm{x} \overrightarrow{\mathrm{c}}+\overrightarrow{\mathrm{c}} \mathrm{x} \overrightarrow{\mathrm{a}}=0\)
    –  Area of any quadrilateral whose diagonal vectors are \(\overrightarrow{\mathrm{d}}_{1} \& \overrightarrow{\mathrm{d}}_{2}\) is given by \(\frac{1}{2}\left|\overrightarrow{\mathrm{d}}_{1} \mathrm{x} \overrightarrow{\mathrm{d}}_{2}\right|\)

Shortest Distance Between Two Lines

If two lines in space intersect at a point, then obviously the shortest distance between them is zero. Lines which do not intersect & are also not parallel are called Skew lines. For Skew lines the direction of the shortest distance would be perpendicular to both the lines. The magnitude of the shortest distance vector would be equal to that of the projection of \(\overrightarrow{\mathrm{AB}}\) along the direction of the line of shortest distance, \(\overrightarrow{\mathrm{L} \mathrm{M}}\) is parallel to \(\overrightarrow{\mathbf{p}} \times \overrightarrow{\mathbf{q}}\) i. e.
\(\overrightarrow{\mathrm{LM}}=| \text { Projection of } \overrightarrow{\mathrm{AB}} \text { on } \mathrm{LM}|=| \text { Projection of } \overrightarrow{\mathrm{AB}} \text { on } \overrightarrow{\mathrm{p}} \times \overline{\mathrm{q}}|=\left|\frac{\overrightarrow{\mathrm{AB}} \cdot(\overrightarrow{\mathrm{p}} \times \overrightarrow{\mathrm{q}})}{\overrightarrow{\mathrm{p}} \times \overrightarrow{\mathrm{q}}}\right|=\left|\frac{(\overrightarrow{\mathrm{b}}-\overrightarrow{\mathrm{a}}) \cdot(\overrightarrow{\mathrm{p}} \mathrm{x} \overrightarrow{\mathrm{q}})}{|\overrightarrow{\mathrm{p}} \times \overrightarrow{\mathrm{q}}|}\right|\)

1. The two lines directed along \(\overrightarrow{\mathrm{p}} \& \overrightarrow{\mathrm{q}}\) will intersect only if shortest distance = 0 i.e. \((\vec{b}-\vec{a}) \cdot(\vec{p} x \vec{q})=0 \quad \text { i.e. }(\vec{b}-\vec{a})\) lies in the plane containing \(\overrightarrow{\mathrm{p}} \& \overrightarrow{\mathrm{q}} \rightarrow[(\overrightarrow{\mathrm{b}}-\overrightarrow{\mathrm{a}}) \overrightarrow{\mathrm{p}} \mathrm{q}]=0\)
2. If two lines are given by \(\overrightarrow{\mathrm{r}}_{1}=\overrightarrow{\mathrm{a}}_{1}+\mathrm{K} \overrightarrow{\mathrm{b}} \& \overrightarrow{\mathrm{r}}_{2}=\overrightarrow{\mathrm{a}}_{2}+\mathrm{K} \overrightarrow{\mathrm{b}}\) i.e. they are parallel then,
\(\mathrm{d}=\left|\frac{\overrightarrow{\mathrm{b}} \mathrm{x}\left(\overrightarrow{\mathrm{a}}_{2}-\overrightarrow{\mathrm{a}}_{1}\right)}{|\overrightarrow{\mathrm{b}}|}\right|\)

Scalar Triple Product / Box Product / Mixed Product

– The scalar triple product of three vectors \(\overrightarrow{\mathbf{a}}, \overrightarrow{\mathbf{b}} \& \overrightarrow{\mathbf{c}}\) is defined as:
\(\overrightarrow{\mathbf{a}} \times \overrightarrow{\mathrm{b}} \overrightarrow{\mathrm{c}}=|\overrightarrow{\mathrm{a}}| \overrightarrow{\mathrm{b}}|\overrightarrow{\mathrm{c}}|\) sin θ cos φ where θ is the angle between \(\overrightarrow{\mathrm{a}} \& \overrightarrow{\mathrm{b}}\&\phi\) is the angle between \(\overrightarrow{\mathrm{a}} \times \overrightarrow{\mathrm{b}} \& \overrightarrow{\mathrm{c}}.\) It is also defined as \([\vec{a} \vec{b} \vec{c}]\) , spelled as box product.
– Scalar triple product geometrically represents the volume ofthe parallelopiped whose three couterminous
edges are represented by
\(\overrightarrow{\mathbf{a}}, \overrightarrow{\mathbf{b}} \& \overrightarrow{\mathbf{c}} \mathbf{i} . \mathbf{e} \cdot \mathbf{V}=[\overrightarrow{\mathbf{a}} \overrightarrow{\mathbf{b}} \overrightarrow{\mathbf{c}}]\)
– In a scalar triple product the position of dot & cross can be interchanged i.e.
\(\overrightarrow{\mathbf{a}} \cdot(\overrightarrow{\mathbf{b}} \mathbf{x} \overrightarrow{\mathbf{c}})=(\overrightarrow{\mathbf{a}} \mathbf{x} \overrightarrow{\mathbf{b}}) \overrightarrow{\mathbf{c}} \text {OR}[\overrightarrow{\mathbf{a}} \overrightarrow{\mathbf{b}} \overrightarrow{\mathbf{c}}]=[\overrightarrow{\mathbf{b}} \overrightarrow{\mathbf{a}} \mathbf{a}]=[\overrightarrow{\mathbf{c}} \overrightarrow{\mathbf{a}} \overrightarrow{\mathbf{b}}]\)
– \(\overrightarrow{\mathrm{a}} \cdot(\overrightarrow{\mathrm{b}} \mathrm{x} \overrightarrow{\mathrm{c}})=-\overrightarrow{\mathrm{a}} \cdot(\overrightarrow{\mathrm{c}} \mathrm{x} \overrightarrow{\mathrm{b}}) \text { i.e. }[\overrightarrow{\mathrm{a}} \overrightarrow{\mathrm{b}} \overrightarrow{\mathrm{c}}]=-[\overrightarrow{\mathrm{a}} \overrightarrow{\mathrm{c}} \overrightarrow{\mathrm{b}}]\)
– If \(\vec{a}=a_{1} \hat{i}+a_{2} \hat{j}+a_{3} \hat{k} \quad ; \vec{b}=b_{1} \hat{i}+b_{2} \hat{j}+b_{3} \hat{k} \quad \& \vec{c}=c_{1} \hat{i}+c_{2} \hat{j}+c_{3} \hat{k}\) then.
In general, if
\(\overrightarrow{\mathrm{a}}=\mathrm{a}_{1} \overrightarrow{\mathrm{l}}+\mathrm{a}_{2} \overrightarrow{\mathrm{m}}+\mathrm{a}_{3} \overrightarrow{\mathrm{n}} \quad ; \quad \overrightarrow{\mathrm{b}}=\mathrm{b}_{1} \overrightarrow{\mathrm{l}}+\mathrm{b}_{2} \overrightarrow{\mathrm{m}}+\mathrm{b}_{3} \overrightarrow{\mathrm{n}} \quad \& \quad \overrightarrow{\mathrm{c}}=\mathrm{c}_{1} \overrightarrow{\mathrm{l}}+\mathrm{c}_{2} \overrightarrow{\mathrm{m}}+\mathrm{c}_{3} \overrightarrow{\mathrm{n}}\)
then
\([\overrightarrow{\mathrm{a}} \overrightarrow{\mathrm{b}} \overrightarrow{\mathrm{c}}]=\left| \begin{array}{lll}{\mathrm{a}_{1}} & {\mathrm{a}_{2}} & {\mathrm{a}_{3}} \\ {\mathrm{b}_{1}} & {\mathrm{b}_{2}} & {\mathrm{b}_{3}} \\ {\mathrm{c}_{1}} & {\mathrm{c}_{2}} & {\mathrm{c}_{3}}\end{array}\right|[\overrightarrow{1} \mathrm{m} \overrightarrow{\mathrm{n}}]\)  where \(\vec{\ell}, \overrightarrow{\mathrm{m}} \& \overrightarrow{\mathrm{n}}\) are non coplanar vectors.
–  If \(\overrightarrow{\mathbf{a}}, \overrightarrow{\mathbf{b}}, \overrightarrow{\mathbf{c}}\) are coplanar ⇔ \([\vec{a} \vec{b} \vec{c}]=0.\)
Note: If \(\overrightarrow{\mathrm{a}}, \overrightarrow{\mathrm{b}}, \overrightarrow{\mathrm{c}}\) are non − coplanar then \([\vec{a} \vec{b} \vec{c}]>0\) for right handed system & \([\vec{a} \vec{b} \vec{c}]<0\) for left handed system
– [i j k] = 1
– \([\mathrm{Ka} \overrightarrow{\mathrm{b}} \overrightarrow{\mathrm{c}}]=\mathrm{K}[\overrightarrow{\mathrm{a}} \overrightarrow{\mathrm{b}} \overrightarrow{\mathrm{c}}]\)
– \([(\vec{a}+\vec{b}) \vec{c} \vec{d}]=[\vec{a} \vec{c} \vec{d}]+[\vec{b} \vec{c} \vec{d}]\)
–  he volume ofthe tetrahedron OABC with O as origin & the pv’s ofA, B and C being \(\overrightarrow{\mathrm{a}}, \overrightarrow{\mathrm{b}} \& \overrightarrow{\mathrm{c}}\) respectively is given by \(\mathrm{V}=\frac{1}{6}[\overrightarrow{\mathrm{a}} \overrightarrow{\mathrm{b}} \overrightarrow{\mathrm{c}}]\)
–  The positon vector of the centroid of a tetrahedron if the pv’s of its angular vertices are \(\overrightarrow{\mathbf{a}}, \overrightarrow{\mathbf{b}}, \overrightarrow{\mathbf{c}} \& \overrightarrow{\mathbf{d}}\) are given by \(\frac{1}{4}[\vec{a}+\vec{b}+\vec{c}+\vec{d}]\)
Note that this is also the point of concurrency of the lines joining the vertices to the centroids of the opposite faces and is also called the centre of the tetrahedron. In case the tetrahedron is regular it is equidistant from the vertices and the four faces of the tetrahedron .
Remember that:
\(\left[ \begin{array}{ccc}{\vec{a}-\vec{b}} & {\vec{b}-\vec{c}} & {\vec{c}-\vec{a}}\end{array}\right]=0 \quad \& \quad[\vec{a}+\vec{b} \quad \vec{b}+\vec{c} \quad \vec{c}+\vec{a}]=2[\vec{a} \vec{b} \vec{c}]\)

Vector Triple Product

Let \(\overrightarrow{\mathbf{a}}, \overrightarrow{\mathbf{b}}, \overrightarrow{\mathbf{c}}\) be any three vectors, then the expression \(\overrightarrow{\mathbf{a}} \times(\overrightarrow{\mathbf{b}} \times \overrightarrow{\mathbf{c}})\) is a vector & is called a vector triple product.
Geometrical Interpretation Of\(\overrightarrow{\mathbf{a}} \times(\overrightarrow{\mathbf{b}} \times \overrightarrow{\mathbf{c}})\)
Consider the expression \(\overrightarrow{\mathbf{a}} \times(\overrightarrow{\mathbf{b}} \times \overrightarrow{\mathbf{c}})\) which itself is a vector, since it is a cross product of two vectors \(\vec{a} \&(\vec{b} x \vec{c}) . \text { Now } \vec{a} \times(\vec{b} \times \vec{c})\) is a vector perpendicular to the plane containing \(\vec{a} \&(\vec{b} x \vec{c})\) but \(\overrightarrow{\mathrm{b}} \times \overrightarrow{\mathrm{c}}l\) is a
vector perpendicular to the plane \(\overrightarrow{\mathrm{b}} \& \overrightarrow{\mathrm{c}},\) therefore \(\overrightarrow{\mathbf{a}} \times(\vec{b} \times \overrightarrow{\mathbf{c}})\) is a vector lies in the plane of \(\overrightarrow{\mathrm{b}} \& \overrightarrow{\mathrm{c}}\) and perpendicular to \(\overrightarrow{\mathrm{a}}.\) Hence we can express [/latex]\overrightarrow{\mathbf{a}} \times(\overrightarrow{\mathbf{b}} \mathbf{x} \overrightarrow{\mathbf{c}})[/latex] in terms of \(\overrightarrow{\mathrm{b}} \& \overrightarrow{\mathrm{c}}\) i.e. \(\vec{a} \times(\vec{b} \times \vec{c})=x \vec{b}+y \vec{c}\) where x & y are scalars.
– \(\overrightarrow{\mathbf{a}} \times(\overrightarrow{\mathbf{b}} \times \overrightarrow{\mathbf{c}})=(\vec{a} \cdot \overrightarrow{\mathbf{c}}) \overrightarrow{\mathbf{b}}-(\overrightarrow{\mathbf{a}} \cdot \overrightarrow{\mathbf{b}}) \overrightarrow{\mathbf{c}}\)
– \((\vec{a} \times \vec{b}) \times \vec{c}=(\vec{a} \cdot \vec{c}) \vec{b}-(\vec{b} \cdot \vec{c}) \vec{a}\)
– \((\vec{a} \times \vec{b}) \times \vec{c} \neq \vec{a} \times(\vec{b} \times \vec{c})\)

Linear Combinations / Linearly Independence and Dependence of Vectors

Given a finite set of vectors \(\overrightarrow{\mathbf{a}}, \overrightarrow{\mathbf{b}}, \overrightarrow{\mathbf{c}}, \ldots\) then the vector \(\overrightarrow{\mathbf{r}}=\mathbf{x} \overrightarrow{\mathbf{a}}+\mathbf{y} \overrightarrow{\mathbf{b}}+\mathbf{z} \overrightarrow{\mathbf{c}}+\ldots \ldots \ldots\) is called a linear
combination of \(\overrightarrow{\mathrm{a}}, \overrightarrow{\mathrm{b}}, \overrightarrow{\mathrm{c}}, \ldots \ldots\) for any x, y, z …… ∈ R. We have the following results:

  • Fundamentaltheorem In Plane:  Let \(\vec{a}, \vec{b}\) be non zero , non collinear vectors . Then any vector \(\overrightarrow{\mathrm{r}}\) coplanar with \(\overrightarrow{\mathbf{a}}, \overrightarrow{\mathbf{b}}\)  can be expressed uniquely as a linear combination of \(\overrightarrow{\mathrm{a}}, \overrightarrow{\mathrm{b}}\)  i.e. There exist some unique x ,y ∈ R such that \(x \vec{a}+y \vec{b}=\vec{r}\)
  • Fundamental Theorem In Space: Let \(\overrightarrow{\mathbf{a}}, \overrightarrow{\mathbf{b}}, \overrightarrow{\mathbf{c}}\) be non−zero, non−coplanar vectors in space. Then any vector \(\overrightarrow{\mathrm{r}},\) can be uniquily expressed as a linear combination of \(\overrightarrow{\mathbf{a}}, \overrightarrow{\mathbf{b}} \overrightarrow{\mathbf{c}}\) i.e. There exist some unique x,y ∈ R such that \(x \vec{a}+y \vec{b}+z \vec{c}=\vec{r}.\)
  • If \(\overrightarrow{\mathbf{x}}_{1}, \overrightarrow{\mathbf{x}}_{2}, \ldots \ldots \overrightarrow{\mathbf{x}}_{\mathbf{n}}\) are n non zero vectors, & k1, k2, …..kn are n scalars & if the linear combination
    \(k_{1} \vec{x}_{1}+k_{2} \vec{x}_{2}+\ldots \ldots k_{n} \vec{x}_{n}=0 \Rightarrow k_{1}=0, k_{2}=0 \ldots k_{n}=0\) then we say that vectors \(\vec{x}_{1}, \vec{x}_{2}, \ldots . . \vec{x}_{n}\) are
    Linearly Independent Vectors.
  • If \(\vec{x}_{1}, \vec{x}_{2}, \ldots . . \vec{x}_{n}\) Linearly Independent then they are said to be Linearly Dependent vectors i.e. if
    \(\mathbf{k}_{1} \overrightarrow{\mathbf{x}}_{1}+\mathbf{k}_{2} \overrightarrow{\mathbf{x}}_{2}+\ldots \ldots+\mathbf{k}_{\mathbf{n}} \overrightarrow{\mathbf{x}}_{\mathbf{n}}=\mathbf{0}\) & if there exists at least one kr≠ 0 then \(\overrightarrow{\mathbf{x}}_{1}, \overrightarrow{\mathbf{x}}_{2}, \ldots \ldots \overrightarrow{\mathbf{x}}_{\mathbf{n}}\) are said to be Linearly Dependent.
    Note:
    –  If \(\vec{a}=3 i+2 j+5 k \text { then } \vec{a}\) is expressed as a Linear Combination of vectors \(\hat{\mathrm{i}}, \hat{\mathrm{j}}, \hat{\mathrm{k}}.\) Also, \(\overrightarrow{\mathrm{a}}, \hat{\mathrm{i}}, \hat{\mathrm{j}}, \hat{\mathrm{j}}\) form a linearly dependent set of vectors. In general , every set of four vectors is a linearly dependent system.
    – \(\hat{\mathbf{i}}, \hat{\mathbf{j}}, \hat{\mathbf{k}}\) are Linearly Independent set of vectors. For
    \(\mathrm{K}_{1} \hat{\mathrm{i}}+\mathrm{K}_{2} \hat{\mathrm{j}}+\mathrm{K}_{3} \hat{\mathrm{k}}=0 \Rightarrow \mathrm{K}_{1}=0=\mathrm{K}_{2}=\mathrm{K}_{3}\)
    –  Two vectors \(\overrightarrow{\mathrm{a}} \& \overrightarrow{\mathrm{b}}\) are linearly dependent ⇒ \(\overrightarrow{\mathrm{a}}\) is parallel to \(\vec{b} \text { i.e. } \vec{a} \times \vec{b}=0 \Rightarrow\) linear dependence of \(\overrightarrow{\mathrm{a}} \& \overrightarrow{\mathrm{b}}.\) Conversely if \(\vec{a} \times \vec{b} \neq 0 \text { then } \vec{a} \& \vec{b}\) are linearly independent.
    –  If three vectors \(\overrightarrow{\mathbf{a}}, \overrightarrow{\mathbf{b}}, \overrightarrow{\mathbf{c}}\) are linearly dependent, then they are coplanar i.e. \([\vec{a}, \vec{b}, \vec{c}]=0,\) conversely, if \([\vec{a}, \vec{b}, \vec{c}] \neq 0\) , then the vectors are linearly independent.

Coplanarity Of Vectors

Four points A, B, C, D with position vectors \(\vec{a}, \vec{b}, \vec{c}, \vec{d}\) respectively are coplanar if and only if there exist scalars x, y, z, w not all zero simultaneously such that \(x \vec{a}+y \vec{b}+z \vec{c}+w \vec{d}=0\) where, x + y + z + w = 0.

Reciprocal System Of Vectors

If \(\overrightarrow{\mathbf{a}}, \overrightarrow{\mathbf{b}}, \overrightarrow{\mathbf{c}} \quad \& \vec{a}^{\prime} \vec{b}^{\prime} \vec{c}^{\prime}\) are two sets of non coplanar vectors such that \(\vec{a} \cdot \vec{a}^{\prime}=\vec{b} \cdot \vec{b}^{\prime}=\vec{c} \cdot \vec{c}^{\prime}=1\) then the two systems are called Reciprocal System of vectors.
Note:\(a^{\prime}=\frac{\vec{b} x \vec{c}}{[\vec{a} \vec{b} \vec{c}]} ; b^{\prime}=\frac{\vec{c} x \vec{a}}{[\vec{a} \vec{b} \vec{c}]} ; c^{\prime}=\frac{\vec{a} \times \vec{b}}{[\vec{a} \vec{b} \vec{c}]}\)

Equation Of A Plane

  • The equation \(\left(\vec{r}-\vec{r}_{0}\right) \vec{n}=0\) represents a plane containing the point with p.v.\(\overrightarrow{\mathrm{r}}_{0}\) where \(\overrightarrow{\mathrm{n}}\) is a vector normal to the plane.
    \(\overrightarrow{\mathbf{r}} \cdot \overline{\mathbf{n}}=d\) is the general equation of a plane.
  • Angle between the 2 planes is the angle between 2 normals drawn to the planes and the angle between a line and a plane is the compliment of the angle between the line and the normal to the plane.

Application of Vectors

  1. Work done against a constant force \(\overrightarrow{\mathrm{F}}\) over adisplacement \(\overrightarrow{\mathbf{s}}\) is defined as \(\overrightarrow{\mathrm{W}}=\overrightarrow{\mathrm{F}} \overrightarrow{\mathrm{s}}\)
    Applications of Vectors
  2. The tangential velocity \(\overrightarrow{\mathrm{V}}\) of a body moving in a circle is given by \(\overrightarrow{\mathrm{V}}=\overrightarrow{\mathrm{w}} \times \overrightarrow{\mathrm{r}} \text { where } \overrightarrow{\mathrm{r}}\) is the pv of the point P
  3. The moment of \(\overrightarrow{\mathrm{F}}\) about ’O’ is defined as \(\overrightarrow{\mathrm{M}}=\overrightarrow{\mathrm{r}} \times \overrightarrow{\mathrm{F}}\) where \(\overrightarrow{\mathbf{r}}\) is the pv of P wrt ’O’. The direction of \(\overrightarrow{\mathbf{M}}\) is along the normal to the plane OPN such that \(\overrightarrow{\mathbf{r}}, \overrightarrow{\mathbf{F}} \boldsymbol{\&} \mathbf{\vec { M }}\) form a right handed system.
    Applications of Vectors - Vector Algebra
  4. Moment of the couple \(=\left(\overrightarrow{\mathrm{r}}_{1}-\overrightarrow{\mathrm{r}}_{2}\right) \times \overrightarrow{\mathrm{F}} \text { where } \overrightarrow{\mathrm{r}}_{1} \& \overrightarrow{\mathrm{r}}_{2}\) are pv’s of the point of the application of the forces \(\overrightarrow{\mathbf{F}} \boldsymbol{\boldsymbol { X }}-\overrightarrow{\mathbf{F}}\)

3 -D Coordinate Geometry | Vector Algebra

Vector Algebra Class 12
Distance (d) between two points (x1 , y1 , z1) and (x2 , y2 , z2)
\(d=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2}+\left(z_{2}-z_{1}\right)^{2}}\)

Section Formula:

\(x=\frac{m_{2} x_{1}+m_{1} x_{2}}{m_{1}+m_{2}} \quad ; \quad y=\frac{m_{2} y_{1}+m_{1} y_{2}}{m_{1}+m_{2}} ; z=\frac{m_{2} z_{1}+m_{1} z_{2}}{m_{1}+m_{2}}\) ( For external division take –ve sign )
Direction Cosine and direction ratio’s of a line

Direction cosine of a line has the same meaning as d.c’s of a vector.

(a) Any three numbers a, b, c proportional to the direction cosines are called the direction ratios i.e.
\(\frac{l}{\mathrm{a}}=\frac{\mathrm{m}}{\mathrm{b}}=\frac{\mathrm{n}}{\mathrm{c}}=\pm \frac{1}{\sqrt{\mathrm{a}^{2}+\mathrm{b}^{2}+\mathrm{c}^{2}}}\)
same sign either +ve or –ve should be taken through out.
note that d.r’s of a line joining x1 , y1 , z1 and x2 , y2 , z2 are proportional to x2 – x1 , y2 – y1 and z2 – z1
(b) If θ is the angle between the two lines whose d.c’s are l1 , m1 , n1 and l2 , m2 , n2
cosθ = l1l2 + m1m2+n1n2 hence if lines are perpendicular then l1l2 + m1m2+ n1n2 = 0 if lines are parallel then \(\frac{l_{1}}{l_{2}}=\frac{\mathrm{m}_{1}}{\mathrm{m}_{2}}=\frac{\mathrm{n}_{1}}{\mathrm{n}_{2}}\)
note that if three lines are coplanar then
\(\left| \begin{array}{lll}{l_{1}} & {\mathrm{m}_{1}} & {\mathrm{n}_{1}} \\ {l_{2}} & {\mathrm{m}_{2}} & {\mathrm{n}_{2}} \\ {l_{3}} & {\mathrm{m}_{3}} & {\mathrm{n}_{3}}\end{array}\right|=0\)
Vector Algebrajee maths formulas vectors 6

Projection of join of 2 points on line with d.c’s l, m, n are l (x2 – x1) + m(y2 – y1) + n(z2 – z1)
B. Plane

  1. General equation of degree one in x, y, z i.e. ax + by + cz + d = 0 represents a plane.
  2. Equation of a plane passing through (x1 , y1 , z1) is a (x – x1) + b (y – y1) + c (z – z1) = 0 where a, b, c are the direction ratios of the normal to the plane.
  3. Equation of a plane if its intercepts on the co-ordinate axes are x1 , y1 , z1 is
    \(\frac{x}{x_{1}}+\frac{y}{y_{1}}+\frac{z}{z_{1}}=1\)
  4. Equation of a plane if the length of the perpendicular from the origin on the plane is p and d.c’s of the perpendicular as l , m, , n is lx + my + nz = p
  5. Parallel and perpendicular planes – Two planes a1x + b1y + c1z + d1 = 0 and a2x + b2y + c2z + d2 = 0 are perpendicular if a1a2 + b1b2 + c1c2 = 0  parallel if \(\frac{\mathrm{a}_{1}}{\mathrm{a}_{2}}=\frac{\mathrm{b}_{1}}{\mathrm{b}_{2}}=\frac{\mathrm{c}_{1}}{\mathrm{c}_{2}}\) and coincident if \(\frac{\mathrm{a}_{1}}{\mathrm{a}_{2}}=\frac{\mathrm{b}_{1}}{\mathrm{b}_{2}}=\frac{\mathrm{c}_{1}}{\mathrm{c}_{2}}=\frac{\mathrm{d}_{1}}{\mathrm{d}_{2}}\)
  6. Angle between a plane and a line is the compliment of the angle between the normal to the plane and the
    line . If \(\left.\begin{array}{l}{\text { Line } : \overrightarrow{\mathrm{r}}=\overrightarrow{\mathrm{a}}+\lambda \overrightarrow{\mathrm{b}}} \\ {\text { Plane } : \overrightarrow{\mathrm{r}} \cdot \overrightarrow{\mathrm{n}}=\mathrm{d}}\end{array}\right] \text { then } \cos (90-\theta)=\sin \theta=\frac{\overrightarrow{\mathrm{b}} \cdot \overrightarrow{\mathrm{n}}}{|\overrightarrow{\mathrm{b}}| .|\overrightarrow{\mathrm{n}}|}\)
    where θ is the angle between the line and normal to the plane.
  7. Length of the perpendicular from a point (x1 , y1 , z1) to a plane ax + by + cz + d = 0 is
    \(p=\left|\frac{a x_{1}+b y_{1}+c z_{1}+d}{\sqrt{a^{2}+b^{2}+c^{2}}}\right|\)
  8. Distance between two parallel planes ax + by + cz + d1 = 0 and ax+by + cz + d2 = 0 is
    \(\left|\frac{\mathrm{d}_{1}-\mathrm{d}_{2}}{\sqrt{\mathrm{a}^{2}+\mathrm{b}^{2}+\mathrm{c}^{2}}}\right|\)
  9. Planes bisecting the angle between two planes a1x + b1y + c1z + d1 = 0 and a2 + b2y + c2z + d2 = 0 is  given by
    \(\left|\frac{a_{1} x+b_{1} y+c_{1} z+d_{1}}{\sqrt{a_{1}^{2}+b_{1}^{2}+c_{1}^{2}}}\right|=\pm\left|\frac{a_{2} x+b_{2} y+c_{2} z+d_{2}}{\sqrt{a_{2}^{2}+b_{2}^{2}+c_{2}^{2}}}\right|\)
    Of these two bisecting planes , one bisects the acute and the other obtuse angle between the given planes.
  10. Equation of a plane through the intersection of two planes P1 and P2is given by P1+λP2=0

Straight Line In Space

  1. Equation of a line through A (x1 , y1 , z1) and having direction cosines l ,m , n are
    \(\frac{\mathrm{x}-\mathrm{x}_{1}}{l}=\frac{\mathrm{y}-\mathrm{y}_{1}}{\mathrm{m}}=\frac{\mathrm{z}-\mathrm{z}_{1}}{\mathrm{n}}\) and the lines through (x1 , y1 ,z1) and (x2 , y2 ,z2)
    \(\frac{x-x_{1}}{x_{2}-x_{1}}=\frac{y-y_{1}}{y_{2}-y_{1}}=\frac{z-z_{1}}{z_{2}-z_{1}}\)
  2. Intersection of two planes a1x + b1y + c1z + d1 = 0 and a2x + b2y + c2z + d2 = 0 together represent the unsymmetrical form of the straight line.
  3. General equation of the plane containing the line
    \(\frac{\mathrm{x}-\mathrm{x}_{1}}{l}=\frac{\mathrm{y}-\mathrm{y}_{1}}{\mathrm{m}}=\frac{\mathrm{z}-\mathrm{z}_{1}}{\mathrm{n}}\) is  A (x – x1) + B(y – y1) + c (z – z1) = 0 where Al + bm + cn = 0 .

Line of Greatest Slope
AB is the line of intersection of G-plane and H is the horizontal plane. Line ofgreatest slope on a given plane, drawn through a given point on the plane, is the line through the point ‘P’ perpendicular to the line of intersection of the given plane with any horizontal plane.

Filed Under: CBSE Tagged With: Coplanarity Of Vectors, Direction Cosines, Multiplication of Vector By Scalars, Reciprocal System Of Vectors, Scalar Triple Product, Vector, Vector Addition, Vector Algebra, vector algebra basics, vector algebra class 12, vector algebra class 12 notes, vector algebra class 12 pdf, vector algebra formulas, vector algebra pdf, Vector Equation Of A Line, Vector Product Of Two Vectors, vector space linear algebra, Vector Triple Product, Vectors

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