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Multivariate Calculus
Calculus on Vector Functions:
All laws of calculus can be applied on vector functions. For scalar/vectors, see in "scalars and vectors" in Maths.
Vector func r(t) = f(t)i + g(t)j + h(t)k in 3D space, where i,j,k are x,y,z dir, and f(t), g(t), h(t) are component func which are real valued in terms of parameter "t". vector r(t) is also expressed as <f(t),g(t),h(t)>. Curve C is traced by the terminal point of vector r(t), and is known as the plot or graph of vector r(t). r(t) is written in bold or wih a arrow on top to indicare it's a vector.
Domain of a vector func is the intersection of domains of f, g and h.
Representing any graph by a vector valued func: This can be done by choosing parameter "t", and defining component func in terms of t.
ex: y = x^2 + 1. Choose x=t (may choose anything). y=t^2+1. So, vector r(t) = ti +(t^2+1)j. The direction of the curve is from t=-inf to t=+inf (left to right). IF we choose x=-t, then dir of curve would reverse, i.e from right to left. To sketch a curve given as parametrized func, it's usually eadier to convert it to c,y,z cartesian coordinates, and see if it coresponds to any of the known shapes. Otherwise, nly a computer program may sketch such curves, as they can get complicated.
Limit of Vector func: Limits follow same rule as sclar as each component i,j,k of vector func is a scalar func, so we just take limits of each scalar component func.
Continuity of vector func: It's cont at t=a, if each of it's cmponent func is cont at t=a.
Differentiation/Integration of Vector func:
Derivative: Dt([r(t)] = r'(t) = Lim Δt->0 r(t+Δt) - r(t) / Δt = f'(t)i + g'(t)j + h'(t)k => i.e take differentiation of each component of vector. Similarly higher order differentials may be found by successively differentiating.
Vector func r(t) is smooth on an open interval I, if f'(t), g'(t), h'(t) are cont on I, and r'(t) ≠ 0 (i.e derivative for all components are not 0) for any value of t in the interval I. There is no such requirement in scalar calculus, but shows up in vector calculus. Inline discussion suggests that having drivative as 0 reverses dir of the curve (te slope of curve doesn't keep going in same dir) which poduces cusps, resulting in non smooth func at those points. However, I couldn't find a formal proof for this. What's interesting is that it's not the curve traced by vector, that determines if it's differentiable, but the way the curve is traced, i.e by changing parameters, the same curve may now become undifferentiable.
Ex: Take a plane parabola: (t, t^2, 0). According to the definition, it is smooth, and indeed, a parabola is a curve we would intuitively like to call smooth: At (0,0,0), differentiation is (1,2t,0)=(1,0,0), since it's not 0 for all components at origin, it's smooth => it doesn't take a sudden rough turn. However, consider the same parabola, but parametrize slightly differently: (t^3, t^6, 0). This is not smooth at t=0, as it's diff is (3t^2, 6t^5,0) = (0,0,0) at origin. The way the curve is traced made a difference. Below is one of the links explaiing this:
Source: https://www.physicsforums.com/threads/why-must-dr-dt-never-equal-zero-for-a-smooth-curve.180821/
Properties of derivatives of vector func: Same as in real func for given vector func r(t). All easy to prove by writing vector func in terms of components.
- Dt([f(t)*r(t)] = f'(t)*r(t) + f(t).r'(t). Here f(t) is real func (as simple multiplication operation doesn't exist for 2 vectors).
- Dt([r((f(t))] = r'(f(t))*f'(t). Here f(t) is real fun.
- Dot product (of 2 vectors): Dt([u(t).r(t)] = u'(t)*r(t) + u(t).r'(t). This gives a scalar answer (as dot product is always a scalar)
- Cross product (of 2 vectors): Dt([u(t) X r(t)] = u'(t) X r(t) + u(t) X r'(t). This gives a vector answer (as cross product is always a scalar)
- Dot product: If r(t).r(t) = c (a constant), then r(t) . r'(t) = 0. This is a very important property, as it states that if r(t) itself is constant, then it's derivative, r'(t) will always be orthogonal to r(t). This is what is observed in Kinematics eqn in Physics. If velocity is constant, then it's derivative, acceleration vector is orthogonal to velocity (obvious as if acceleration was in dir of velocity, then velocity would have changed)
Integration:
Integration is same as for scalar func, we integrate each component for both definite and indefinite integral. For indefinite integral, we have to add constant C for each component, so constantbecomes C1i + C2j + C3k = C vector. So, r(t)dt = R(t) + C, where R'(t) = r(t) and both R(t) and C are vector func. If initial cond is given, then we can find vector C.
Displacement, velocity and acceleration:
From eqn above, displacement is curve C plotted by tip of r(t). The derivative of r(t) is velocity vector, v(t), and derivative of v(t) is acceleration vector, a(t).
Vector r(t) can be from anywhere (not necessarily from origin), the curve plotted by r(t) remains same as C, as that's what the moving object is tracing.
v(t) = r'(t) = x'(t)i + y'(t)k + z'(t)k. As Δt -> 0, v(t) becomes tangent to the curve C [as v(t) is pointing in same dir as r(t+Δt) - r(t) ].
a(t) = v'(t) = r''(t) = x''(t)i + y''(t)k + z''(t)k. (double differentiation).
We can also do reverse of diff, i.e integration to determine v(t) and r(t) from a(t) given some initial conditions to eliminate constant vector C.
Projectile motion: This is eqn of a particle thrown at an angle from height h1 and hitting a target at heohgt h2. The curve traced by this motion is a parabola (i.e quadratic eqn).
a(t) = -gj, where g=9.8m/s^2 (or 32 ft/s^2), and t is time
v(t) = ∫ a(t)dt = -gtj + C1
r(t) = ∫ v(t)dt = ∫ [-gtj + C1] dt = -gt^2/2j + C1t + C2
To find C1 and C2, you may use initial cond: v(0) = v0 and r(0) = r0. These are both vectors.
Solving, we get: r(t) = -gt^2/2j + v0t + r0, where v0 and r0 are both vectors
In most cases, initial height and speed along with angle of projection are given, which gives following:
r(0) = +h1j
v(0) = ucos(θ)i + usin(θ)j, where u is the initial speed, and theta is angle of projection measured from horizontal surface.
So, r(t) = -gt^2/2j + v0t + r0, = -gt^2/2j + utcos(θ)i + utsin(θ)j + h1j = [ucos(θ)]t i + [h1 + [usin(θ)]t -gt^2/2] j
v(t) = -gtj + [ucos(θ)]i + [usin(θ)]j = [ucos(θ)] i + [usin(θ) - gt] j
From rqn above, we can very easily find all the stats w/o knowing any physics, just calculus info is enough:
- Max height: Max height is achieved when velocity vector has no vertical component, i.e V(t1) = x i + 0j => u*sin(θ) - gt1 =0 => t1 = u*sin(θ)/g. Substitute t1 in r(t) to get max height.
- Total time to travel: It's when j component of r(t) =0 => [h1 + [usin(θ)]t -gt^2/2] = 0 => gives time t. Ignre the value with -ve sign before the sq root.
- Total distance traveled: It's when j component of r(t) =0 => We got time from above eqn, we subtitute in r(t) = [ucos(θ)]t with t from above eqn.
- If projectile motion hits object at height h2, then r(t1) = Di + h2j => h2 = [h1 + [usin(θ)]t -gt^2/2] => [(h1-h2) + [usin(θ)]t -gt^2/2] =0 => we get t here, with h1 replaced by (h1-h2).
Functions of more than one variable:
Chapter 13:
If we have a func like z=f(x,y)= x^2+y^2, it's a func of 2 var. We can graph this in 3D space with points (x,y,z), which will be a curved surface. The domain is all possible values of (x,y) that the func can take, and range is all possible values of z.
Level curve or contour lines are a 2D map of multiple lines where along each line, the value of f(x,y) is constant. Drawing these lines on 2D map gives an idea of how the 3D surface looks like. Similar to Level curve, we've level surface when we extend the func to 3 var, i.e f(x,y,z). It's a graph where f(x,y,z) = contant along those surfaces (this shows in a 3D plot).
ex: z=f(x,y) = √ (16 - 4*x^2 - y^2)
- Domain: Here the eqn under sq root ≥ 0 for it to be real number. Hence domain is (16 - 4*x^2 - y^2) ≥ 0. The boundary of this is an ellipse x^2/4 + y^2/16 = 1. All points on or inside this ellipse are in the domain, i.e x^2/4 + y^2/16 ≤ 1.
- Range: Range of this func is all possible z values. Smallest z value is 0, which happens on the boundary of the ellipse. The eqn under sq root can't be > 16, as the smallest possible value of x^2 and y^2 is 0. So, range of z is 0 ≤ z ≤ 4.
- To plot the graph of (x,y,z), we get z^2 = 16 - 4*x^2 - y^2 => x^2/4 + y^2/16 + z^16 = 1, with 0 ≤ z ≤ 4 which is eqn of ellipsoid with only upper half included due to militaion of z being +ve number only.
Limits and Continuity:
Limits for 2 var func follows same principle as that for 1 var, For limit to exist for 1 var func, f(x) at x=a, we calc value of f(x) as x approaches "a" from left and right, and if they are both equal to L, then limit exists and is equal to L. In func of 2 var, f(x,y), for limit to exist at f(x0,y0), the value of func should equal to L from all directions, not just from 2 axis.
Formal defn:
Lim (x->x0, y->y0) f(x,y) = L if for each ε > 0, there exists a δ > 0, st |f(x,y) - L| < ε whenever 0 < R < δ, where R = √ [ (x-x0)^2 + (y-y0)^2] (i.e R is the radius of the open circle centered at (x0,y0)
Graphically, this implies that for any point (x,y) ≠ (x0,y0) in the disk of radius δ, the value of f(x,y) lies b/w (L - ε) and (L + ε).
It's sometimes not easy to prove whether a limit in func of multiple var, exists or not. We may get limit by directly substituting where undefined values as 0/0 or ∞/∞ doesn't show up. In some cases, we may show that limit doesn't exist by approaching along x-axis, and then along y-axis or may be along y=x, and show that it results in diff values.
Continuity: A func of 2 var f(x,y) is continuous at pt (x0,y0) in a open region R if Lim (x->x0, y->y0) f(x,y) = f(x0,y0). To be cont in whole region R, func needs to be cont at all points in R.
Removable discontinuity: Some func have a limit eist at a point, but the value doesn't exist at that particular pt. For such func, we can make them cont, by defining the value of that func at that pt to be equal to the limit. Sunc discontinuity is called Removable discontinuity. For some func that discontinuity can't be removed, and is called non-removable discontinuity.
If 2 func f and g are cont at (x0,y0), then below are also cont:
- Scalar multiple: kf
- sum/diff: f+g, f-g
- Product: f*g
- quotient f/g provided g(x0,y0) ≠ 0
Composite func: If func h is cont at (x0,y0), and g is cont at h(x0,y0), then composite func g o h = g(h(x,y) is cont at (x0,y0), i.e Lim (x->x0, y->y0) g(h(x,y)) = g(h(x0,y0)). NOTE: here h is a func of 2 var, while g is a func of 1 var.
The limit and continuity defn can be extended to 3 var func, i.e f(x,y,z) in the same way s 2 var func.
Partial derivatives: When we differentiate a func of mult var wrt only 1 var, treating all other var as constant, we call it partial derivatives and are defined as below, provided limit exists.
1st partial derivative wrt x is fx = ∂f/∂x = Lim Δx->0 [f(x+Δx,y) - f(x,y)] / Δx => This denotes the slope of the curve in x direction.
1st partial derivative wrt y is fy = ∂f/∂y = Lim Δy->0 [f(x, y+Δy) - f(x,y)] / Δy => This denotes the slope of the curve in y direction.
Partial derivative of func of n var can be defined in similar way as above => differentiate wrt one var, and hold all other var as constant.
Higher order Partial derivatives: We can find 2nd, 3rd and so on partial derivatives provided such derivatives exist. It's defined depending on the order in which partial differentiations are done.
2nd order partial derivative wrt x is fxx = ∂/∂x (∂f/∂x) = ∂2f/∂x2 . Similarly 2nd order partial derivative wrt y is fyy = ∂/∂y (∂f/∂y) = ∂2f/∂y2
Mixed partial derivatives:
Diff 1st wrt x and then wrt y is fxy = ∂/∂y (∂f/∂x) = ∂2f/∂y∂x.
Reversing the order, we can Diff 1st wrt y and then wrt x is fyx = ∂/∂x (∂f/∂y) = ∂2f/∂x∂y.
Thm: if both fxy and fyx are cont in an open disc R, then for every (x,y) in R, fxy = fyx. Similarly for third and higher order mixed partial derivatives, the order of diff doesn't matter, as long they are all differntiated wrt same var. i.e fxzz = fzxz = fzzx
Increments and Differentials:
For func of 2 var z=f(x,y), then increment Δz = f(x+ Δx,y+ Δy) - f(x,y) (similar to the one for single var, where dy = f'(x)dx)
Writing diff dx=Δx, dy=Δy, dz=Δz, total diff of z is dz = (∂z/∂x)*dx + (∂z/∂y)*dy = fx(x,y)dx + fy(x,y)dy
For 4 var, w=f(x,y,z,u), dw = (∂f/∂x)*dx + (∂f/∂y)*dy + (∂f/∂z)*dz + (∂f/∂u)*du = fx(x,y,z,u)dx + fy(x,y, z, u)dy + fz(x,y,z,u)dz + fu(x,y, z, u)du.
Differentialiability Thm: Func f(x,y) is diff at (x0,y0) if it can be written in the form Δz = fx(x0,y0)Δx + fy(x0,y0)Δy + ε1*Δx + ε2*Δy where ε1, ε2 -> 0 as Δx,Δy -> 0. Func is diff in region R if it's diff at every pt in R
NOTE sufficent cond for diff: Above req is diff than that for 1 var func. For 1 var func, the func is diff at that pt, if derivative exists. For 2 var func, just the existence of partial derivatives ( fx , fy) at that pt is not enough to guarantee diff. Both partial derivatives ( fx , fy) will need to be cont too at that pt for the func to be diff. Corollary is always true => i.e if a func is diff at (x0,y0), then it's cont at (x0,y0).
For whole region R, partial derivatives need to be cont at all points in R for the func to be diff in R.
Linear Approximation: Similar to 1 var to approximate the func in vicinity of a pt (x0,y0) => dz = (∂z/∂x)*dx + (∂z/∂y)*dy. Here dx=Δx, dy=Δy, dz=Δz
Chain Rule: Chain rule is similar to the one for single var. It can be extended to more than 2 var too. 2 cases:
- One indep var "t": w=f(x,y) where x and y are defined as func of one other independent var "t", x=g(t), y=h(t), where g and h are diff func of "t, then w is a diff func of t, given by
- dw/dt = (∂w/∂x)*dx/dt + (∂w/∂y)*dy/dt
- Two indep var "s,t": w=f(x,y) where x and y are defined as func of 2 other independent var "s,t", x=g(s,t), y=h(s,t). If all partial derivatives exist, then partial derivatives of w wrt s,t, is given by
- (∂w/∂s) = (∂w/∂x)*(∂x/∂s) + (∂w/∂y)*(∂y/∂s)
- (∂w/∂t) = (∂w/∂x)*(∂x/∂t) + (∂w/∂y)*(∂y/∂t)
Implicit partial differentiation (PD): Chain rule can be used to find PD of func defined implicitly, in a much easier way, than trying to find derivative directly. Consider a func F(x,y)=0, where it's assumed that y=f(x). We could dy/dx by differentiating f(x), which may or may not be possible. There in another neat way:
Let w = F(x,y) = F(x,f(x)) = 0=> dw = Fx(x,y)dx + Fy(x,y)dy, also dw/dx = 0 (since diff of const=0 is 0). => dw/dx = Fx(x,y) + Fy(x,y)dy/dx = 0 => dy/dx = -Fx(x,y)/Fy(x,y)
NOTE: Always remember that when writing func as F(x,y), y is not an indep var. It's a func of x, i.e when x changes, y also changes accordingly based on the eqn. If y were indep, then dy/dx would be equal to 0, since changing x would have no relation to y. Even though we didn't assume y to be dependent on x in above proof, it worked out since
Similarly for func of 3 var, F(x,y,z)=0, and assuming z=f(x,y) and if we want to find PD ∂z/∂x and ∂z/∂y, then we can use multiple methods. Using normal differentiation, ∂z/∂x = fx(x,y), and ∂z/∂y = fy(x,y). However, if we don't have a way to find f(x,y), then we can employ same technique as above. Note that z is not indep var, but dependent on (x,y). Define w=F(x,y,z)=0 => dw/dx = Fx(x,y,z) + Fy(x,y,z)dy/dx + Fz(x,y,z)dz/dx = 0 => Now since x,y are indep var, dy/dx=0 => Fx(x,y,z) + Fz(x,y,z)dz/dx = 0 => dz/dx = -Fx(x,y,z) / Fz(x,y,z). Since y is indep var, so dz/dx = ∂z/∂x, hence ∂z/∂x = -Fx(x,y,z) / Fz(x,y,z).
Similarly ∂z/∂y = -Fy(x,y,z) / Fz(x,y,z).
Directional derivative:
Let z=f(x,y) be a func, and "u" be a unit vector in xy plane, given by u=(cos Θ)i + (sin Θ)j, where Θ is the angle the vector makes with +ve x-axis.We saw that we can find slope of curve in x dir and y dir by taking PD fx and fy. To find slope in dir parallel to u vector (in dir Θ), it can be proved that slope is fx*(cos Θ) +fy*(sin Θ).
This is called directional derivative of f(x,y) in dir of unit vector u, denoted by Duf(x,y) = fx(x,y)*(cos Θ) +fy(x,y)*(sin Θ).
NOTE: u is unit vector. If another non unit vector is given, then you need to convert it to unit vector to get on form of (cos Θ)i + (sin Θ)j. Also, before you find dir derivative, you need to prove that PD are continuous and hence differentiable (just existence of PD doesn't prove they are differentiable).
Gradient of func: Gradient is a vector and is defined as grad f(x,y) = ∇f(x,y) = fx(x,y)i +fy(x,y)j , where i and j are components along x and y axis. Note that this vector is in x-y plane and not in 3D space.
Directional derivative is dot product of grad of Func and the unit vector.
Duf(x,y) = ∇f(x,y) . u (easy to prove, as ∇f(x,y) = fx(x,y)i +fy(x,y)j, and u=(cos Θ)i + (sin Θ)j, doing dot product gives the result)
Dir of max/min ascent/descent: There are many directional derivatives at a given point in a curve (since angle can go from 0 to 360). Most of the times, we are interested in finding the dir where f(x,y) inc most rapidly (i.e the dir of steepest ascent) or the dir where f(x,y) dec most rapidly (i.e the dir of steepest descent). We are trying to find max/min value of Duf(x,y), which happens in the dir of ∇f(x,y)
Thm:
- If ∇f(x,y) = 0, then Duf(x,y) = 0 for all u.
- Dir of max inc of f is given by +∇f(x,y). The max value of Duf(x,y) = + || ∇f(x,y) ||
- Dir of min inc of f is given by -∇f(x,y). The min value of Duf(x,y) = - || ∇f(x,y) ||
- If f(x,y) is diff at (x0,y0) and ∇f(x0,y0) ≠ 0 , then ∇f(x0,y0) is normal to the level curve thru (x0,y0). => The rate of change in the dir of unit tangent vector is 0.
- All above thm can be expanded to func of 3 var and more.
Tangent plane and normal lines to a surface:
Let's say z=f(x,y), which reps a surface S. We can write this as F(x,y,z) = f(x,y) - z = 0, which reps he same same surface S, but now it's a level Surface with constant value = 0. For any surface, we can find a normal line to the surface, as well as a tangent plane to the surface.
For a pt P (x0,y0,z0) on Surface S, given by eqn F(x,y,z)=0, let's find tangent line/surface and normal line.
Tangent and Notmal Lines on the surface: Let C be a curve on S passing thru pt P. We can write it's eqn as vector r(t) = x(t)i + y(t)j + z(t)k , then for all t, F(x(t),y(t),z(t))=0 => dF/dt = Fx(x,y,z).dx/dt + Fy(x,y,z).dy/dt + Fz(x,y,z).dz/dt = 0 (by diff both sides) => ∇F(x,y,z) . r'(t) = 0 (dot product of 2 vectors) => ∇F(x,y,z) and r'(t) are orthogonal to each other. At pt P (x0,y0,z0), ∇F(x0,y0,z0) . r'(t0) = 0. Since r'(t0) is a tangent vector, it implies that ∇F(x0,y0,z0) is a normal vector perpendicular to all the the tangent lines at S.
Line thru P having dir of ∇F(x,y,z) is called the normal line to S at P. Parametric eqn for this normal line is (x-x0)/a = (y-y0)/b = (z-z0)/c , where a,b.c are the direction numbers (coeff of i, j, k) in ∇F.
Tangent plane: The plane thru P that is normal to ∇F(x,y,z) is called the tangent plane to S at pt P. To find eqn of this plane, consider vector v = (x-x0)i + (y-y0)k + (z-z0)k, where (x,y,z) is any pt that lies on this plane. Then ∇F(x0,y0,z0) . v = 0 (dot product of 2 vectors is 0 as v is orthogonal to ∇F) => Eqn of plane is Fx(x0,y0,z0)*(x-x0) + Fy(x0,y0,z0)*(x-x0) + Fz(x0,y0,z0)*(x-x0) = 0