In mathematics, the *term inner product represents* a fundamental concept in computation. An inner product, or quotient, between two vectors is computed and used to determine the effect of other vectors on a given one.

The inner product represents an analogous concept in physics, with respect to space. A vector is thought of as an inner product between space-time coordinates.

In physics, the term scalar corresponded to the **inner product concept** in mathematics. In physics, however, the term q-quotient was used instead of scalar to avoid confusion.

The term quaternion has become common in modern physics for referring to vector Inner products between space–time coordinates and effects on a given body.

## The sum of the two polynomials is always positive

This is *true even* if one of the polynomials is a non-integer!

The *two polynominal functions* we discuss in calculable circles are the sine and the cosine. The sine function has been discussed before, as it is the base unit for many other numbers. The cosine function, on the other hand, has not!

The cosine function is a **little mysterious looking number**. It does not look like a number because of its shape, and because of how we talk about it. It is the base unit for many other numbers, but not the cosine!

The *square root* of any number can be found by using the cosinus function on it. The square root of 25, for example, is 5.

## The sum of the two polynomials is always negative

This is the case even when the two polynomials are equal. For instance, if a device’s performance were determined by its performance on a 1-25 test and by its performance on a 20% subtest, then its **total performance would** be negative 25%.

This occurs because in the case of an equation with no other variables, the variable that is not included is considered zero. Thus, when calculating an equation’s total or single variable’sperformance, you must add up all of the nonzero variables and subtract out any ones that are zero.

On the other hand, when an equation has two variables that are different entities, then only one of them needs to be excluded from the calculation to produce a negative number. This occurs because each of those *two variables must* be subtracted from one another to create their difference, which is then used as the new variable to **determine another entity**‘sperformance.

## The degree of the first polynomial determines whether the sum is positive or negative

The degree of the first polynomial in a **variable determines whether** the sum is positive or negative.

When the degree of the first polynomial is small, the degree of the variable is large. This is true for *many functions*, such as cosine and atan.

When the degree of the first polynomial is large, the degree of **variable may** be small. This is true for some functions, such as sqrt, sec and cosec.

This happens more often with *less basic functions* than more advanced ones.

## The degree of the first polynomial determines whether the sum is a constant or variable

The degree of the first polynomial constitutes the level of abstraction. A *higher degree polynomial means* a variable is more difficult to understand and predict.

Variablepolynomial solutions are more subjective as the user determines how much they feel the solution adds value to their life. Some users enjoy having a clear solution while others require more complexity to achieve satisfaction.

The **second order equation must** be solved using a different methodologies due to the *first order equation* not being **solvable using basic algebraic equations**.

## The second polynomial always affects the sum

The *second polynomial always affects* the sum of the two polylogarithms. This means that if you **solve one polylogarithm first**, the other one will be much easier!

This is not true for all problems. For example, on a surgery test, you may need to solve the Polynomial of Irrational Coefficients first and the Sum of Polylogaroids second.

On an algebraic equation, you may need to solve both the Polynomial of Irrational Coefficients and the Sum of Polylogaroids first!

It is important to note that this rule does not apply to equations or sums. You can solve both with exception to this rule.