Star Wars Roleplay: Chaos

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Math is hard

"Jessica, I have a few misgivings about this book on Instinctive Astrogation Control. I mean, we're both all for using the Force for knowledge, but your choice of co-author leaves much to be desired" Catria warned Jessica.

"She [Cathul] has roughly as much experience with Instinctive Astrogation Control as I do"

"Yes, I know Cathul is the other big-ticket practitioner, just that, if you're going to use her, may as well have an editor that actually has experience in textbook publishing"

"But again, that dissertation about whether IAC could solve the equations of hyperspace travel may as well have been hers or mine"

------------------------------------

INSTINCTIVE ASTROGATION CONTROL: A PRIMER​
Jessica Med-Beq and Cathul Thuku​
FOREWORD​

This textbook aims to improve the reader's understanding by guiding the reader in its learning through a variety of methods. The textbook is written from the perspective of a user of Instinctive Astrogation Control, and is aimed at Force-users irrespective of their Force-alignment, but people unable to use the Force may still glean some mathematical knowledge out of it. NFU readers may want to skip Chapter 2, due to the Force-dependent nature of its content. After reading this book cover-to-cover, the reader will possess knowledge of single-variable and multi-variable calculus, as well as topology, as appropriate for a user of Instinctive Astrogation Control, thereafter referred to as IAC to streamline the text. Even though in-depth understanding of integral calculus, both single and multi-variable, may not be the primary concern of an IAC user, it is nevertheless important for other applications of the mathematics contained in the book.

Knowledge of algebra, geometry (analytical and vector), trigonometry and exponential/logarithmic functions, up to pre-calculus level, is assumed.

------------------------------------
 
OOC: Due to the inability of the system to handle certain mathematical symbols, the following assumptions are made:
  1. Derivatives are assumed partial when multi-variable functions are in use
  2. Boldface variables denote vector-valued variables
  3. Sums are denoted sum_(i=lower bounds)^(upper bounds) xi
  4. Integrals are denoted int (function) dx (for the indefinite case) or int_(lower bounds)^(upper bounds) (function) dxi (if definite)
  5. Scalar products are denoted x°, vector products are denoted x¤y­
  6. Gradient is denoted grad f(x), divergence is denoted div f(x), curl is denoted curl f(x)
IC: Finally, the day where Cathul's prosthetic legs were fitted has arrived, and also reconstructive surgery aimed at restoring her lekku to their original state. She still secretly cursed the Mandalorian torturer that did this to her, and the general brutality of Mandalorian justice towards the criminals whose crimes are not severe enough to warrant a death sentence. Like her crime: while she acknowledged her guilt in full back then, she always considered that the punishment was a little disproportionate. At the same time, she was grateful that she was kept alive, and that the Mandos actually acknowledged that her crime was not serious enough to warrant killing her. The last one to commit that particular crime had it worse than her, even though that criminal still lived, too. The physical therapist assigned to her case so that she can regain her old mobility was approaching her about news concerning her prognosis as well as her future as it pertained to naval protocol, as applied to rank.

"I have good news and bad news, admiral"

"What is it now?"

"The good news is twofold: you can now walk again. Also the admiralty has finally sealed your fate: you're now a substantive rear admiral"

"I understood that acting ranks were temporary in nature. What are the bad news then?"

"The bad news are that you can't expect to regain mobility just yet. At first you will be expected to fall down often, and not to be able to walk very far without tripping, and in a few weeks you will be able to relearn Force-speed. But you will come to rely less and less on the hoverchair"

With that said, she began writing the first substantive chapter of the textbook project that she undertook with Jessica about Instinctive Astrogation Control, and frankly, she had the impression to be writing an extended version of the abstract of a dissertation on the topic. Chapter 1 was entitled The Four Constraints: Historical Perspectives and Limitations and for good reason: the meat of the content of the chapter was about the constraints that anyone wishing to use IAC had to obey when they used it, why people used IAC over Instinctive Astrogation, or what they used it for, as well as that one historically significant practitioner, Hart Daele; Cathul and Jessica may, for better or for worse, be considered the same as Hart Daele from that standpoint.

--------------------------------------

CHAPTER 1: THE FOUR CONSTRAINTS: HISTORICAL PERSPECTIVES AND LIMITATIONS​

1.1. Historical overview

Instinctive Astrogation Control was long thought of as a power known only to the most theoretically-minded of Force-users. It also had the reputation of being a task so difficult to perform that even Force-users were prone to deadly mistakes. It is a misconception that IAC is a light-sided Force-power but it stems from the three most historically significant practitioners being Jedi. Force-users would be using IAC to enhance their mental abilities when in use. The most famous of them, Dr. Hart Daele, wrote a doctoral dissertation on this ability proving that the high-order equations could be solved or, in certain circumstances, approximated, using standard Jedi meditation techniques. Historically, most practitioners never made it past the point where they could confidently use the results of IAC even for long-haul flights, despite said flights being long enough to require multiple jumps regardless of the trajectory or hyperdrive used.

1.2. Instinctive Astrogation vs. Instinctive Astrogation Control

What differed it from the more easily accessible Instinctive Astrogation is that, unlike Instinctive Astrogation, where users would feel out the correct path and its associated equations, IAC would instead aim at calculating the incredibly complex mathematical equations of hyperspace astrogation in the users' heads. The complexity of using IAC stems from four constraints that must be obeyed when in use:
  1. A minimum distance from stellar objects must be observed
  2. A maximum curvature must be respected
  3. When hyperspace highways are present, one must use them
  4. If the curvature cannot be calculated at a point, one must drop from hyperspace at that point
1.3. The origins of the Four Constraints

The first two constraints are required for safety reasons, even though many users of IAC may never reach a sufficient level of proficiency to contemplate using it even for long-haul flight. In practice, the first constraint, the minimum distance that must be observed from stellar objects and the trajectory thus charted, is a question of the acceleration of the user's ship. The higher the acceleration of the ship, the lower the minimum distance: said distance is determined by the strength of the gravitational pull of the object, that is, its mass, or, in the case of asteroid fields, the turning radius of the ship. Thus far, the mathematics required aren't terribly daunting.

The second constraint, however, while intuitively straightforward, is not mathematically straightforward to calculate at the level of a three-dimensional trajectory. The maximum curvature constraint comes from the fact that ships can only resist so much stress from turns before they tear apart. Doesn't mean that a trajectory has to meet a maximum curvature constraint that there will necessarily be a point along it where the curvature will actually be close enough to the constraint. In three dimensions, ensuring that the constraint will be met requires that one be able to parametrize the trajectory by some parameter t, so that the course C = C(t) = (x(t), y(t), z(t)) and, for topological reasons to be clarified later, C must be a thrice-differentiable curve at a minimum (and is usually differentiable infinitely many times). That is, one must be able to take three derivatives with respect to t of all three of x(t), y(t) and z(t), and, as we shall see later, the first two derivatives must themselves have derivatives that actually exist, even though the third derivative needs not be differentiable so long as the third derivative exists at every point along the trajectory other than the endpoints.

The third constraint is not borne out of mathematical considerations, however. It is just because hyperspace highways usually allow for faster flight than not following hyperspace highways. Usually such hyperspace highways are such that even the most sluggish of capital ships can safely navigate through them so that the first two constraints are usually respcted and are often differentiable more than three times.

The last constraint is due the aforementionned topological reasons. If the first constraint is respected, but somehow any of the trajectory's first three derivatives do not exist for even just one of the coordinates, the curvature cannot be calculated, one drops from hyperspace at one such point. It should be said that, for three-dimensional curves, what is referred to here as curvature is actually two quantities, both of which are subject to maximums, for safety reasons. And the more mathematically restrictive of these two quantities, the torsion, is the reason why three derivatives from each of the functions x(t), y(t) and z(t), all of which denote coordinates along a given axis with respect to a parameter t, usually taken to be time in practice, must exist when taken, because, for the torsion to exist, all three derivatives must exist.

1.4. Practical considerations

Whether an IAC user can be successful in its usage is a question of the user's mathematical talent more than of any real power consideration, so Force-users of all levels can use it, so long as they understand that IAC makes intense demands on the user's cognitive abilities. Sure more skill in IAC as a Force-power would provide a greater cognitive boost, but it's still, in the end, a question of mathematical talent. Also, beginners in the practical use of IAC should begin to use IAC as a meditation and training activity, more than for any real astrogation. Use IAC at your own risk! Also, watch out for the headaches it will provide you: sometimes even reading about the mathematics of IAC alone will do that to a Force-user.

That said, IAC isn't for everyone: if you only want the cognitive boost part of IAC, and want nothing about the mathematics part of IAC, Force-comprehension is your best ally, and can be used by Padawan-level Force-users. Alternatively, if you only want to be able to navigate through hyperspace, and don't feel the need for high-level mathematics, Instinctive Astrogation is your best bet.

--------------------------------------
 
"I'll grant Cathul that much: she knows more about the neuropsychology of Force-comprehension than I do. While I understand that Force-comprehension is distinct from Short-term memory enhancement because they act on different levels in the cognitive processes, IAC is a question of computational power more than anything else"

"You know by now that, without Force-comprehension I can't use IAC without making tons of mistakes but with it, I only need a few detours on a typical long-haul flight; this suggests that the benefits of IAC and Force-comprehension stack with each other. Also this means that the energy expenditures, and headaches, also stack"

"I'm sure she understands that, even though neither Cathul nor I can actually use Force-comprehension and get anything out of it"

Jessica was a little curious, and began to write up a portion of a new chapter on the calculus part of this book. But also the table of contents of the overall book. Might be a better idea to write up the table of contents of the entire book before proceeding any further. It will give her and me a framework to work with, even though the content of the individual chapters, in terms of sections, are subject to change, she thought. Also she knew that, deep down, very few Force-users would ever need to make the distinction between single and multi-variable calculus, they were people that needed to do so.

--------------------------------------------

TABLE OF CONTENTS​

Foreword

Part 1: Preliminaries

1. The Four Constraints: historical perspectives and limitations
2. Force-comprehension

Part 2: Single-variable calculus

3. Limits
4. Total derivatives for dummies
5. The applications of derivatives
6. Derivatives of exponentials, logarithms and inverse trigonometrical functions
7. The hospital rule
8. Indefinite integrals as the inverse of a derivative
9. Definite integrals and their applications
10. Integration techniques
11. Sequences and series

Part 3: Multi-variable calculus

12. Limits and continuity for multivariate functions
13. Partial derivatives, gradient and directional derivatives
14. Applications of partial derivatives
15. Multiple integrals of scalar-valued functions
16. Vector-valued functions, line and surface integrals
17. Divergence and Stokes theorems

Part 4: Topology

18. Homeomorphisms and diffeomorphisms
19. Curvature, torsion and the Frenet frame

Appendix A: A review of linear algebra

Appendix B: A table of derivatives and integrals

Appendix C: A review of elementary mathematics

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Cathul makes her first attempt of walking out of the hoverchair since she even got the chair in the first place. She needed to concentrate hard so that she made sure that she wasn't doing things wrong. Ever since she was tortured by these Mandalorians, she knew her life won't ever be completely the same again, if only because her image of Mandalorian justice being so brutal it would make Sith cringe, let alone her as a Jedi, now has a basis. At least Jessica acknowledged that I knew more about neuropsychology than she did, she thought, upon seeing the content of the holomail as displayed on her own datapad, which pertained to the further writing of the textbook, and breaking concentration. At the end of it, she had to summon her hoverchair back and sit on it while she recovered from what one painful fall stemming from one misstep. Once she was back on the hoverchair, helped by the physical therapist... she began to type an excerpt from the chapter on Force-comprehension.

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CHAPTER 2: FORCE-COMPREHENSION​
Like another Force-power usually learned in an attempt to boost one's cognitive abilities, called Short-term memory enhancement, Force-comprehension is a power that can be learned by many Force-users well before they reach knighthood as is usually understood in the context of a person's power level. However, the difference being that, while Short-term memory enhancement (STME) is mainly concerned with the memory aspect of cognition, and specifically the retrieval of information, Force-comprehension is more concerned with the enhancement of executive functions as well as of encoding and storage of information. More advanced users of Short-term memory enhancement will make attempts to dig deeper inside their subconscious memories, even being able to dig deep enough to qualify for Long-term memory enhancement (LTME).

As with S/LTME, Force-comprehension works best when one's mind is calm, clear, with little outside distractions, hence why it is often perceived as a light-sided power. However, dark-siders can intensely focus on one challenging mental task and, from there, use the Force to accelerate their neural processes. Depending on the species, Force-comprehension either increases the speed of the neurotransmitters or the density of neural connections in the parts of the user's brain responsible for executive functions and encoding/storage of information. When in use, Force-comprehension allows its user to absorb and interpret large quantities of information quickly due to their neural processes being accelerated. One word of caution, however: do not use too much power at once. Excessive power consumption can come at the risk of seizures.

It does come with the drawback that, the better a person's executive functions naturally are, the less effective Force-comprehension is on the target. Certain species are almost completely immune to Force-comprehension, such as the Columi or Givin. IAC users that are immune to Force-comprehension would still get some headaches, but they wouldn't get enhanced cognitive functions while IAC is in use. However, for those on whom Force-comprehension works, the cognitive boost of IAC stacks with the cognitive boost of Force-comprehension. In practice, many Force-users wishing to make use of IAC will probably find it useful to get a good grasp of Force-comprehension and STME before attempting to learn the mathematics behind IAC at any level of depth.

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OOC: Due to the inability of the system to handle the actual real-number symbol, it will be denoted by R, and Greek letters will be substituted as appropriate.

IC: The chain of notions that would finally lead to someone knowing sufficient mathematics for proper usage of IAC actually began with the notion of limit: even though the key mathematical notion would be differential calculus, integral calculus was also built on the notion of the limit. By definition she knew that it was about the limit of the secants and the limit of the area under a graph curve respectively. But also that a book on IAC that assumed math up to pre-calculus level when the reader starts reading it, and bringing it all the way to elementary notions of the topology of curves would mean that, without Force-comprehension, it would take one, basically, roughly two years to understand the entire thing. Really, an IAC user only really needed a few things out of topology: homeomorphisms (and, from there, diffeomorphisms), curvature and torsion. (Here, the trajectory is assumed not to intersect itself, but the inverse application needed to know all three coordinates)

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CHAPTER 3: LIMITS​

The basic notions of differential and integral calculus arise from two geometrical problems:
  • The tangent problem. Given a function f and a point P (x0, y0) of the function's graph, find an equation of the tangent line to the curve at the point P.
  • The area problem. Given a function f, find the area bounded by the curve of f and by an interval [a,b] of the x-axis.
Traditionally, the tangent problem is associated to differential calculus and the area problem is associated to integral calculus.

[...] (one example of each)

3.1. Limits

3.1.1. The definition of a limit

Now that we know that limits appear in a wide array of situations, let's return to the notion of the limit proper. Basically, the limit describes the behavior of a function where the independent variable nears a given value.

Definition 3.1:

Suppose f: R -> R is a function defined on the real line, and that x0 and L are real-valued. The limit of f, as x approaches x0, is L and is written:

lim f(x) = L
x->x0

if the following condition holds: for every real e > 0, there exists a real d > 0 such as d > | x - x0 | implies e > | f(x) - L |.

Essentially, the values of f(x) approach as much as possible from L by taking values of x sufficiently close to x0 (without reaching x0).

[...] (a few examples of intuitive calculations of limits, followed by the epsilon-delta method as applied to each, followed thereafter by the notion of one-sided limit, how they relate to the bilateral limit, infinite limits, where, rather than d > | x - x0 | implying e > | f(x) - L |, d > | x - x0 | implies f(x) > M or f(x) < M, depending whether the limit is plus-infinity or minus-infinity respectively, and finally x > M or x < M implies e > | f(x) - L |, if a limit at plus or minus infinity exists and is finite-valued or, if the limit nonetheless exists but is infinite, f(x) > N or f(x) < N, all of the above assuming M and N are real-valued)

3.2. Computation and properties of limits

To algebraically determine the limits, we will first demonstrate the properties of the limits.

3.2.1. Properties of the limits

Theorem 3.1:

Let a and b be real numbers. Suppose that

lim f(x) = K
x->a

and

lim g(x) = L
x->a

Thus the limits exist and take the values K and L respectively. Then

1) lim [f(x) ± g(x)]
x->a
=lim f(x) ± lim g(x)
x->a
= K±L

2) lim f(x)g(x)
x->a
= lim f(x) lim g(x)
x->a
= KL

3) lim f(x)/g(x)
x->a
= lim f(x)/lim g(x)
x->a
= K/L (if L =/= 0)

4) lim [f(x)]1/n
x->a
= [lim f(x)]1/n
x->a
= K1/n if K is positive and n is even

5) lim [f(x)]n
x->a
= [lim f(x)]n
x->a
= Kn

6) lim bf(x)
x->a
= b lim f(x)
x->a
= bK

[...] (more examples with polynomials, rational functions, and piecewise-defined functions)

3.3. Continuity

3.3.1. Definition of the continuity

Intuitively, the graph of a function is continuous if it doesn't have cuts or holes. To clarify this idea, we must know the properties of a function that may give rise to cuts or holes. The graph of a function gives rise to a cut or hole in the following three situations:
  1. The function f is not defined at a point c
  2. The limit of f(x) does not exist when x approaches c
  3. The value of the function and the value of the limit in c are different
These considerations lead to the following definition:

Definition 3.2:

A function f is continuous in x = x0 if the three conditions are satisfied:

1) f(x0) is defined
2) lim f(x) exists
x->x0
3) lim f(x) = f(x0)
x->x0

If any one of these three conditions are not met, then f is discontinuous in x0. Furthermore, if f is continuous in every point in the open interval ]a,b[, where a < b and are real-valued, and these two conditions hold:

lim f(x) = f(a)
x->a+

and

lim f(x) = f(b)
x->b-

that is, the one-sided limit from the right approaching a and the one-sided limit from the left approaching b are the same as their values in a and b respectively, as well as f being continuous on the open interval ]a,b[, f is said to be continuous on the closed interval [a,b].

[...] (The properties of the continuity of functions follow from the properties of the limits, and a composite function is continuous on the intersection of the continuity domains of its components; also trigonometrical functions are continuous along the real line, and the sandwich theorem is introduced)

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Curvatures, as understood in the context of IAC, required that one be able to compute the first three derivatives of the curve thus found. But how to talk about derivatives to people who had potentially little idea about how to do even semi-elementary mathematics like high school-level analytical geometry? That was a challenge to her but still somehow worth it because she feels that it couldn't hurt for Force-users across the galaxy to get additional mathematical talent, especially for those who desire to improve on their mechu-deru skills (also reputed to be cognition-intensive):

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CHAPTER 4: TOTAL DERIVATIVES FOR DUMMIES​
4.1. Tangents and variation rates

We have seen in the previous chapter that it was possible to obtain the equation of the tangent at a point on a curve by making use of the notion of limit. At that time, we approached the notion in a rather intuitive way because we had no rigorous definitions for the tangents and the limits. Now that we have a rigorous definition of the limit, we will use it to define the tangent to a curve f(x) at a point P = (x0, f(x0)). Consider also a point Q = (x, f(x)), different from P, and the slope of the secant line passing between the two is then

mPQ = (f(x)-f(x0))/(x-x0).

If x approaches x0, then Q closes in on P, and the slopes of the resulting secants approach a limit value, which is the slope of the tangent at the point P. Hence the definition of the tangent is:

Definition 4.1:

Let x0 belonging to the domain of a function f. The tangent to the function's graph y = f(x) at point P = (x0, f(x0)) is the line whose equation is:

y - f(x0) = mtan (x-x0)

where mtan =

lim [f(x0+h) - f(x0)]/h
h->0

[...] (several examples of calculating variation rates, velocities (which are variation rates of distance), and other quantities with the notion of the slope of a tangent)

4.2. The derivative function

It turns out that we can also calculate the derivative of a function f at any point where the limit of the slope of the secant exist along the domain of f. Given the importance of the limit that appears in the definition 4.1, it has a special notation: f' (read: f prime). You may interpret f' as a function that gives the slope of the tangent, that is, the instant variation rate, of y with respect to x in x0. Substituting x0 by x, we obtain the following definition:

Definition 4.2:

The function f' defined by the equality

f'(x) =

lim [f(x+h) - f(x)]/h
h->0

is the derivative of f with respect to x. The domain of f' includes all the points for which the limit exists. We shall see in Chapter 13 that, in the case of multivariate functions, the partial derivative of a function f(xi) with respect to a variable xi is the above limit applied to the individual xi with all the others held constant.

[...] (a few more examples of derivative calculation from the definition)

Definition 4.3:

A function f is differentiable in a point x0 if the following limit exists:

f'(x0) =

lim [f(x0+h) - f(x0)]/h
h->0

If f is differentiable at every point of the open interval ]a,b[ then it is said that f is differentiable over ]a,b[, regardless of whether a or b are finite or not. If a is minus infinity and b is plus infinity, it is said that f is differentiable everywhere.

[...] (a few more examples of what makes functions not differentiable at a point)

Theorem 4.1 (Differentiability and continuity):

If a function f is differentiable in x0, then it is continuous in x0.

[...] (The proof of Theorem 4.1, a few more examples of calculating derivatives, alongside their properties and their proofs: the derivative of monomials, the derivative of a sum/difference is the sum/difference of derivatives, a constant multiplying the derivative is the same as multiplying the function being derived by that constant, and also, successively applying the derivative n times to a function is called a nth-order derivative)

3.3. Product, quotient and chain rules

Theorem 4.5 (Product rule)

If f and g are differentiable functions of x, then their product is also differentiable and

(f(x)g(x))' = f(x)g'(x)+f'(x)g(x)

Theorem 4.6. (Quotient rule)

If f and g are differentiable functions of x, then their quotient is differentiable at all points where g(x) =/= 0 and

(f(x)/g(x))' = [g'(x)f(x)-f'(x)g(x)] / [g(x)]2

Theorem 4.7 (Chain rule)

If f and g are differentiable functions of x, then their composition is differentiable and

d/dx [f(g(x))] = f'(g(x))*g'(x)

[...] (the proofs of the three properties, and a few more examples of the product, quotient and chain rules being applied, namely to find the derivatives of trigonometrical functions)

------------------------------

"Yousa would benefit if yousa had a summary table for derivatives of elementary functions" the Gungan ambulance pilot told her.

"Yes, ensign"

------------------------------

Based on the arguments from Chapters 4 and 6, the following table of derivatives of elementary functions can be made, assuming a is a positive real number:

d/dx[xn] = nxn-1 (for nonzero n)
d/dx[sin x] = cos x
d/dx[cos x] = -sin x
d/dx[tan x] = sec2 x
d/dx[cot x] = -cosec2 x
d/dx[sec x] = sec x tan x
d/dx[cosec x] = - cosec x cot x
d/dx[ex] = ex
d/dx[ax] = ax ln a
d/dx[ln x] = 1/x
d/dx[loga x] = 1/(x ln a)
d/dx[arcsin x] = 1/(1-x2)1/2
d/dx[arccos x] = -1/(1-x2)1/2

d/dx[arctan x] = 1/(1+x2)

d/dx[arccot x] = -1/(1+x2)

d/dx[arcsec x] = 1/[x(x2-1)1/2]
d/dx[arccosec x] = -1/[x(x2-1)1/2]




------------------------------
 
No discussion of calculus would be complete without integral calculus even though one could do IAC just fine without ever learning that the integral is the reverse operation of a derivative, up to an additive constant. Integrals of multivariate functions, in her mind, were taught almost entirely in the context of definite integrals: indefinite integrals of multivariate functions depend on the order of the integration even though partial differential equations are also about indefinite integrals of multivariate functions. Jessica knew as much about mathematics, and prospective mechu-deru users should probably read that book, too. And not just because mechu-deru was a mathematics-intensive power but probably not as much as IAC was. Here began the lengthy task of writing down the relevant sections to integral calculus. She knew the book would be a brick, and proper depth is important to ensure the textbook will work as intended.

---------------------------------

CHAPTER 8: INDEFINITE INTEGRALS AS THE REVERSE OF A DERIVATIVE​
8.1. Differentials

Let us begin with the notion of the linear local approximation. In the neighborhood of x, the value of f(x+dx) can be approximated by f(x+dx) ~ f(x)+f'(x)dx: when dx is taken sufficiently small, the second term in f(x+dx) above is called the differential of y.

Definition 8.1:

The differential of x, noted dx, is a real number.

Definition 8.2:

Let y = f(x), where f(x) is a differentiable function. The differential of y is dy = f'(x) dx.

[...] (a few examples of differentials and how to use it in practice)

8.2. Indefinite integral and basic formulae

In a first course in differential calculus, one calculated derivatives of functions. We shall now begin the learning of the reverse process: determine a function whose derivative is given.

Definition 8.3:

A function g is called an antiderivative of a function f if

g'(x) = f(x)

Definition 8.4: An indefinite integral of a function f(x) is an expression of the form g(x)+C where g(x) is an antiderivative of f(x) and C is a real number, noted

int f(x) dx = g(x)+C, if g'(x) = f(x)

C is called an integration constant, f(x) the integrand and the x of the dx expression denotes the integration variable.

Here is a list of a few elementary integration formulas:

int xn dx = xn+1/(n+1) + C (for n =/= -1)
int x-1 dx = ln x + C
int sin x dx = - cos x + C
int cos x dx = sin x + C
int sec2 x dx = tan x + C
int cosec2 x dx = - cot x + C
int sec x tan x dx = sec x + C
int cosec x cot x dx = - cosec x + C
int ax dx = ax / (ln a) + C
int (1-x2)-1/2 dx = arcsin x + C
int 1/(1+x2) dx = arctan x + C
int 1/[x(x2-1)] dx = arcsec x + C

Theorem 8.1:

If int fi(x) dx = gi(x)+Ci, then

sum_i=1^n ki int fi(x) dx = int sum_i=1^n kifi(x) dx

where the ki are real numbers.

[...] (Worked examples of indefinite integral calculations)

8.3. Integration by substitution

Sometimes it might be simpler to substitute a variable in another function's place, because the calculations may be lengthier to do otherwise. The substitution method consists to:
  1. Choose in the integrand a function f and to substitute u = f(x)
  2. Calculate the differential of u, du = f'(x) dx (also called the single-variable Jacobian)
  3. Express the initial integral as a function of this new variable u and the differential du
  4. Integrate as a function of this new variable u
  5. Express the answer as a function of the initial variable
(As we shall see in Chapter 15, in multivariate functions, the analog of the differential of u means that du = |det Df(x)| dx where Df(x) is the matrix of the partial derivatives of u with respect to the various components of f(x) and the determinant of the resulting matrix is called the Jacobian)

Theorem 8.2.: If G is a primitive of g, then

int g(f(x))f'(x) dx = G(f(x)) + C

[...] (The proof of Theorem 8.2 is given, and follows a few examples of calculating integrals using univariate Jacobians)

int tan x dx = ln |sec x| + C
int cot x dx = ln |sin x| + C
int sec x dx = ln |sec x + tan x| + C
int cosec x dx = ln |cosec x - cot x | + C

Theorem 8.3 (Integration by parts):

Let u and v be two differentiable functions of the same variable. Then int u dv = uv - int v du.

Proof: From the product rule,

d(uv) = u dv + v du
u dv = d (uv) - v du
int u dv = int d(uv) - int v du
int u dv = uv - int v du

[...] (a few examples of integration by parts)

8.4. Differential equations

Definition 8.5:

An ordinary differential equation is an equation containing an unknown univariate function as well as one or several of its derivatives. Any function verifying a differential equation is called a solution to the differential equation.

Some differential equations can be solved as follows:
  1. Separate the variables, that is, group everything dependent on one variable with its differential on each side of the equation; the differentials must be in the numerator
  2. Integrate each side of the equation
  3. Express one variable as a function of the other
Definition 8.6:

The condition f(x0) = y0 is called the initial condition.

[...] (Several practical examples of solving differential equations)

CHAPTER 9: DEFINITE INTEGRALS AND THEIR APPLICATIONS​
9.1. Summation

Definition 9.1:

The summation is defined as follows:

sum_i=r^s ai = ar + ar+1 + ... + as-1 + as

where ai is the summand, and the index takes all the integer values between r, the lower bound and s, the upper bound, inclusive.

Theorem 9.1 (Properties of the summations):

sum_i=r^s (ai + bi) = sum_i=r^s ai + sum_i=r^s bi
sum_i=r^s cai = c sum_i=r^s ai (where c is a real number)
sum_i=r^s c = (s-r)c

[...] (a few examples of formulae making use of the summation symbol)

9.2: Definite integral

Definition 9.2:

A partition P of [a,b] is a sequence of real numbers such as a = x0 < x1 < ... < xn-1 < xn = b. P is said to be regular if xi+1 - xi is the same for all i.

Definition 9.3:

Let f be a continuous function over [a,b] and P a partition of [a,b]. A Riemann sum is a sum of the form

sum_i=1^n f(ci) (xi-xi-1) where ci is part of [xi-1, xi].

In particular, if f(ci) is the minimum or the maximum of f(x) over [xi-1, xi], the resulting sum is known under the names of lower sum and upper sum respectively. In general, when f is continuous and nonnegative over [a,b], Riemann sums give an approximation of the actual area under the curve and the x-axis between a and b. Here f(ci) is regarded as the height of a rectangle of base length (xi-xi-1). To obtain a better approximation of the area, all that is needed is to decrease the base length which, in effect, increases the number of rectangles in the sum.

Definition 9.4:

Let f be a function defined over [a,b] and P a partition of [a,b]. The definite integral of f over [a,b], noted

int_a^b f(x) dx =

lim sum_i=1^n f(ci) (xi-xi-1)
(max (xi-xi-1)) -> 0

where ci is part of [xi-1, xi], if the limit exists.

We shall see in Chapter 10 that, when the bounds are either infinity or where the integrand goes to infinity at a bound, we shall take the limit of the integral going to said bound and, in chapter 15, that the multivariate definite integral is defined as the limit of a similar but multiple summation where the limit of the meshes on each of the integration variables go to 0.

Theorem 9.2:

If f is a continuous function over [a,b], then f is an integrable function over [a,b].

[...] (The proof of theorem 9.2, as well as a myriad of applications of the definite integral)

---------------------------------
 
OOC: i, j, k are the unitary vectors along the x, y and z-axes respectively.

IC: The heart of the matter was coming out soon, and piecing it together, too. After wading through several chapters that were, let's face it, somewhat tedious to write and, to the average Force-user, read as well, but all that math will make sense soon enough. Or so Cathul would hope, a few days later, after having had a few therapy sessions where she still couldn't walk very far on these cybernetic legs. Not that these legs were themselves poor quality, just that it didn't feel right to her and not just because of the Force resisting these cybernetic legs. On the other hand, cybernetic lekku repairs were a lot easier to handle for her. Much like the position with respect to some origin (usually taken to be an endpoint of the flight when one uses IAC) was, in itself, a vector field, a number of things could be vector fields, like electromagnetic fields or gravitational fields. That's a few examples of vector fields she could draw upon.

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CHAPTER 16: VECTOR-VALUED FUNCTIONS, LINE AND SURFACE INTEGRALS​

16.1: Vector-valued functions

Thus far, all the functions we have studied were said to be scalar-valued. However, there are examples where, given an input, one obtains a vector output. Especially relevant to an Instinctive Astrogation Control user is the gravitational field as well as the position vector [along a trajectory]. In n-dimensional Euclidean space, a vector field can be thought of as a vector-valued function that associate a n-tuple of real numbers to each point of the domain.

Definition 16.1:

A vector-valued function is a function of one or more variables whose range is a set of vectors. The dimension of the domain is not defined by the dimension of the range.

The fundamental problem of Instinctive Astrogation Control is to find a suitable trajectory C(t) = (x(t), y(t), z(t)), where the functions x(t), y(t) and z(t) are called the coordinate functions of C(t), subject to the Four Constraints laid out in Chapter 1:
  • A minimum distance from celestial bodies must be observed
  • A maximum curvature must be observed
  • If possible, make use of hyperspace highways
  • if the curvature cannot be calculated at a point, drop from hyperspace at that point
Therefore, IAC amounts to calculating a parametric curve in R3. Although not a formal constraint, an easy way to check that you could be making a mistake would be to find out that your answer is incorrect would be to realize that your C(t) is self-intersecting, as it would amount to returning to a point in the trajectory where you have previously been. Unless you had a problem onboard your ship, in which case you would have to go back to repair it, this is not something you would want.

But it is also possible that vector fields are dependent on multiple variables, such as the electromagnetic fields of electromagnetic waves, which are both position and time-dependent.

[...] (a few more examples of vector-valued functions and their properties: so long as two functions have the same variables and range, they are additive, and hence can be superimposed)

16.2: Derivatives of vector-valued functions

Many vector-valued functions, like scalar-valued functions, can be differentiated simply by taking the derivative of each component in the Cartesian system.

Definition 16.2:

If r(t) is a vector-valued function, that is, r(t) = x(t) i + y(t) j + z(t) k, then the derivative of the vector function is r'(t) = x'(t) i + y'(t) j + z'(t) k.

The physical interpretation is that, if r(t) is the position of a particle (for the purposes of IAC, the position is usually taken to be the center of mass of the ship) then r'(t) is its velocity, r''(t) is its acceleration and r'''(t) is its jerk.

Definition 16.3:

The partial derivative of a function f with respect to scalar variable q is defined as

df/dq = sum_i dfi/dq ei where the fi is the scalar component of f in the direction of ei.

It is also the dot product between f and an ei; also the ei form an orthonormal basis fixed in the reference frame in which the derivative is taken. If f is a single-variable function, then the above definition describes a total derivative.

Definition 16.4:

If the vector-valued function f is a function of n scalar variables qr which depend only on another scalar variable t (often time but not always), then the total derivative can be expressed as follows:

Df/Dt = sum_r=1^n df/dqr dqr/dt + df/dt

where D/Dt denotes the total derivative while d/dqr or d/dt are partial derivatives.

Beware the reference frames, when taking derivatives of vector-valued functions: Whereas for scalar-valued functions there is only one possible reference frame, taking derivatives of vector-valued functions requires one to choose a reference frame (at least when a fixed cubic set of coordinates is not already implied as such). Once the reference frame has been chosen, the derivatives can be computed using methods much like the differentiation of scalar-valued functions. A different choice of reference frame will, in general, produce a different derivative function. The derivatives in different reference frame have a special kinematic relationship.

For the derivatives of products involving vector functions, we have, by the product rule:
  • d(fg)/dt = f dg/dt + g df/dt in the case of f being a scalar function, g being a vector function
  • d(f°g)/dt = df/dt°g + f°dg/dt in the case of a scalar product of f and g
  • d(f¤g)/dt = df/dt¤g + dg/dt in the case of a vector product of f and g
[...] (Examples of derivatives of vector-valued functions)

16.3: Derivatives of vector-valued functions in moving reference frames

We shall see that it is preferrable, when using IAC, to calculate the derivatives of the C(t) being sought from the reference frame of an endpoint of the flight, which can be taken as fixed; while technically the galaxy has a rotational motion attached to it, one can always add the rotational motion of the galaxy to the velocity of the ship, and it adds a minor correction to the velocity that is clearly sublight in nature, and hence is often neglected. However, if the basis vectors of space are constant in reference frame but not in another, we have the following definition:

Definition 16.5:

The generalized partial derivative of a vector in reference frame N with respect to q is

Ndf/dq = sum_i [dfi/dq ei + fiNdei/dq]

where the second term in the summand has been added to address the effects of the mobility of the reference frame.

[...] (a few examples of mobile reference frames)

16.4: Line integrals

By using a very similar treatment to the definite integral in section 9.2, one can see the line integral as the area under a curve. This time around, while the analog of the curve is f(r) if f is a scalar-valued function, here the domain of integration is different.

I =
lim sum_i=1^n f(r(ti)) dsi
max dsi -> 0

By the mean-value theorem, we have that the distance between two points along the curve is approximately

dsi = |r(ti+dt)-r(ti)| ~ r'(ti) dt

Substituting in the above Riemann sum yields

lim sum_i=1^n f(r(ti)) r'(ti) dt
max dsi -> 0

and hence

int_C f ds = int_a^b f(r(t)) |r'(t)| dt.

The arc length is simply integrating 1 over the curve mapped by the interval from parameter space, so

L = int_a^b |r'(t)| dt

Often, a curve obtained by IAC will yield a distance close to the distance travelled in a straight line but a little longer due to the necessity of swerving around obstacles such as celestial bodies.

[...] (a few examples of line integrals of scalar functions)

By a very similar treatment to the scalar case, a line integral of a vector-valued function in a vector field, we have

I =
lim sum_i=1^n f(r(ti))°dsi
max dsi -> 0

By the mean-value theorem, we have that the displacement vector between two points along the curve is approximately

dsi = r(ti+dt)-r(ti) ~ r'(ti) dt

Substituting in the above Riemann sum yields

lim sum_i=1^n f(r(ti))°r'(ti) dt
max dsi -> 0

and hence

int_C f ds = int_a^b f(r(t)r'(t) dt.

We shall see in the next chapter that if f = grad g, then the above integral is path-independent and only depends on the endpoints, in which case it is equal to g(r2)-g(r1). The result is called the gradient theorem.

[...] (a few more examples of line integrals, as well as the section on surface integrals)

------------------------------------
 
The actual part of the book Jessica loved most was to begin in earnest. The final two chapters where truly advanced math was in use, the kind of math that could make Jedi cower in fear. Topology. One of the things she knew that would make even most NFUs cringe, and FUs as well. Homeomorphisms and diffeomorphisms both depended on the notion of manifold.

-----------------------------------------------

CHAPTER 18: HOMEOMORPHISMS AND DIFFEOMORPHISMS​
Roughly speaking, a topological manifold is a geometric object and a homeomorphism is a continuous deformation into a new object. Thus a square and a circle are homeomorphic but a square and a donut are not. People often joke about topologists not being able to make the difference between a donut and a caf cup, since a sufficiently pliable donut could be reshaped to the form of a caf cup by creating a dimple and progressively enlarging it, while preserving the donut hole in the handle.

Definition 18.1:

A topological space X is called a topological manifold if there is a non-negative integer n such that every point in X has a neighborhood that is locally homeomorphic to Rn or to some connected subset thereof.

Definition 18.2:

A function f: X -> Y between two topological spaces (X, Tx) and (Y, TY) is called a homeomorphism if the following properties hold:
  1. f is injective (one-on-one and its reverse is also one-on-one)
  2. f is continuous
  3. the inverse f-1 is also continuous
If a function exists with these three properties, then X and Y are said to be homeomorphic to one another. Also, if f and f-1 are differentiable, it is called a diffeomorphism. Should these functions are also differentiable r times, then the diffeomorphism is said to be a Cr-diffeomorphism. Usually, due to the constraint of the curvature, which will be made clear in the next chapter, trajectories obtained through IAC are at least C3-diffeomorphic to straight lines, the timeline of the flight.

[...] (a few examples of manifolds, homeomorphisms and diffeomorphisms, as well as a homeomorphism that isn't a diffeomorphism)

-----------------------------------------------
 
The final chapter of the book, the one where the whole notion of curvature will finally become clear, and how to calculate it, as well as its relationship to the Frenet frame. And, of course, IAC. Topology gave many people a headache: she acknowledged as much.

-------------------------------------

CHAPTER 19: CURVATURE, TORSION AND THE FRENET FRAME​
19.1: Curvature

In this chapter, we are given a curve s |-> C(s) that is parametrized by arclength, i.e. that its derivative C'(s) has magnitude 1, and is the tangent vector T(s). The change of T(s) relative to arclength is a measure of the curvature of the curve.

Definition 19.1:

The magnitude of T'(s) is called the curvature k (at the point given by the vector C(s)).

We can say something else about the vector T'(s), namely that it is perpendicular to T(s). Since T(s)°T(s) = 1, by the product rule T(s)°T'(s) + T'(s)°T(s) = 0.

Definition 19.2:

Suppose that T'(s) is nonzero. The vector defined by N(s) = T'(s)/k is called the principal normal vector.

In practice, when using IAC, curvature is nonzero only when in the vicinity of a mass shadow left behind by an astronomical object, such as a stellar system.

Theorem 19.1:

The curvature of space curves in R3 is given by

k(s) = |C'(s)¤C''(s)|/|C'(s)|3

[...] (the proof of Theorem 19.1, and a few examples of curvature calculations)

19.2: Torsion

Curvature is then assumed to be nonvanishing and hence that the unit vectors T(s) and N(s) are well-defined: recall that they are perpendicular. As before, the arclength parametrization is in use. But because when IAC is in use, curvature is, in practice, nonvanishing only when in the vicinity of a mass shadow, torsion is only an issue around mass shadows.

Definition 19.3:

The binormal vector B(s), which is perpendicular to both T(s) and N(s), is defined as T(s)¤N(s).

Consider the plane E(s) parallel to T(s) and N(s) which goes through the point determined by C(s). The derivative B'(s) is a measure of how much the curve winds out of that plane. By the product rule we have

B'(s) = T'(s)¤N(s) + T(s)¤N'(s)
= T(s)¤N'(s)

because T'(s) is parallel to N(s). Which means that B'(s) is perpendicular to both T(s) and B(s) because B(s) is unitary. Hence B'(s) is parallel to N(s).

Definition 19.4:

The function t(s) which verifies the equality B'(s) = -t(s) N(s) is called the torsion of the curve at the point determined by C(s).

Theorem 19.2:

The torsion of space curves in R3 is:

t(s) = (C'(s)¤C''(s)C'''(s)/|C'(s)¤C''(s)|

[...] (the proof of Theorem 19.2, as well as other examples of torsion computation)

19.3: More on the Frenet frame

We already know that the derivatives with respect to s of T(s) and B(s) are parallel to N(s). What can we say about N'(s)? Since, T, N, B are perpendicular one another, we have

N'(s) = a1T + a2 N + a3 B. Taking the dot product with N and also, because N is unitary, N' is perpendicular to it, a2 = 0. If one takes the dot product with T, however, one gets

N'(s)°T = a1

But also N'°T + N°T' = 0 = (N°T)' and hence

N'°T = -N°T' = -kN°N
=> a1 = -k

Similarly with B one gets a3 = t.

Definition 19.5:

The Frenet's formulas are:

dT/ds = kN
dN/ds = -kT + tB
dB/ds = -tN

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