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dJesse

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In summary, the dimension of the space of vanishing polynomials is C(n+d-2,d-2), which is why you need to subtract this amount.

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dJesse

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rmehta

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For example, if n=2 you know that x^2 + y^2 - 1 = 0 (on the unit sphere), but also, (x^2 + y^2 - 1)x = 0. In general, (x^2 + y^2 - 1)f(x) = 0 for

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kodylau

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chiro

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blue_raver22

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The dimension of a multivariate polynomial space can be calculated by using the formula C(n+d,d), where n is the number of variables and d is the maximum degree of the polynomials in that space. However, when the domain is restricted to the unit ball, the dimension of the polynomial space changes. This is because all polynomials of the form (sum(i=1..n) x_i^2)^p, where p is a natural number, are identical to the polynomial 1. This means that the dimension of the polynomial space is reduced, as all these polynomials can be represented by a single polynomial.

One way to calculate the dimension of the polynomial space in this case is to subtract the cardinality of the set {(sum(i=1..n) x_i^2)^p | p in N} from the original formula C(n+d,d). This is because the set contains all the redundant polynomials that can be represented by the polynomial 1. This approach is supported by one professor.

However, the course text written by another professor suggests a different formula, C(n+d,d)-C(n+d-2,d-2). This formula also takes into account the reduction in dimension due to the restriction of the domain to the unit ball. It is possible that this formula is based on a different approach or perspective.

In any case, it is important to understand the reasoning and assumptions behind each formula in order to accurately calculate the dimension of the polynomial space in a given scenario. Both formulas may be valid in different contexts and it is up to the individual to determine which one is appropriate for their specific problem. It would be beneficial to consult with both professors and try to understand their perspectives in order to gain a better understanding of the concept.

A multivariate polynomial space is a mathematical concept that refers to the set of all polynomials with more than one variable. These polynomials can have different degrees and coefficients, but they all share the common property of having multiple variables.

The dimension of a multivariate polynomial space is determined by the number of variables in the polynomial and the highest degree of those variables. For example, a polynomial with two variables, x and y, and a degree of 3 would have a dimension of 4 (3+1).

The dimension of a multivariate polynomial space is important because it helps us understand the complexity of the space and the number of possible solutions to a system of polynomial equations. It also allows us to determine the number of coefficients needed to fully specify a polynomial in that space.

The dimension of a multivariate polynomial space directly affects its computational complexity. As the dimension increases, the number of possible solutions and coefficients also increases, making it more difficult to solve for specific solutions or find patterns within the space.

Yes, the dimension of a multivariate polynomial space can be infinite. This occurs when the number of variables and the degree of those variables are both infinite. However, in practical applications, the dimension is often finite and can be determined by the specific problem at hand.

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