# Jim Blinn's Corner: Notation, Notation, Notation

By Jim Blinn

The 3rd access within the **Jim Blinn's Corner** sequence, this can be, just like the others, a convenient compilation of chosen installments of his influential column. yet the following, for the 1st time, you get the "Director's Cut" of the articles: revised, improved, and greater types of the originals. What's replaced? superior mathematical notation, extra diagrams, new ideas. What is still an analogous? every thing you've come to depend upon: immediately solutions, irreverent variety, and cutting edge considering. this is often Jim Blinn at his best-now even better.

**Highlights**

- positive aspects 21 extended and up to date installments of "Jim Blinn's Corner," courting from 1995 to 2001, and not earlier than released in booklet form.

- comprises "deleted scenes"-tangential explorations that didn't make it into the unique columns.

- information how Blinn represented planets in his well-known JPL flyby animations.

- Explores a wide selection of alternative subject matters, from the concrete to the theoretical: meeting language optimization for parallel processors, unique utilization of C++ template instantiation, algebraic geometry, a graphical notation for tensor contraction, and his hopes for a destiny global.

*Features 21 accelerated and up to date installments of "Jim Blinn's Corner," relationship from 1995 to 2001, and not ahead of released in booklet form.

*Includes "deleted scenes"-tangential explorations that didn't make it into the unique columns.

*Details how Blinn represented planets in his well-known JPL flyby animations.

*Explores a wide selection of different issues, from the concrete to the theoretical: meeting language optimization for parallel processors, unique utilization of C++ template instantiation, algebraic geometry, a graphical notation for tensor contraction, and his hopes for a destiny international.

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**Additional info for Jim Blinn's Corner: Notation, Notation, Notation**

The opposite hiccup is while E = H = zero. right here we ﬁnd that w1 =−w2 = F 2 + G 2 and that γ + β is undeﬁned. the unique matrix should have been of the shape A B F G = C D G −F this can be our uniformly scaled reflect reﬂection a couple of line at an perspective of (γ− β) 2 to the x axis. back, we get that the angles β and γ aren't specified, yet needs to easily fulfill γ − β = tan−1 (G/F). We now think that the factoring in Equation (5. 14) is often attainable. Now think what occurs once we move a unit circle via it. it's going to ﬁrst rotate (no swap in shape), then scale via the singular values (now Summary of Invariants it’s an ellipse), after which rotate back (ellipse tilted at an angle). In different phrases, the semimajor and semiminor axes of the ellipses of Figures five. 1, five. three, and five. nine are the singular values of the matrices. And the perspective of tilt of the ellipse equals γ. Invariants of SVD How do the singular values ﬁt into the matrix invariant photograph? placing the rotational invariants (E, H, F2 + G2) into Equation (5. 15), we will see that (w1, w2, γ + β) also are rotationally invariant. we will be able to additionally see this through noting rotation of a matrix will upload an perspective to β whereas it subtracts an analogous attitude from γ, the entire whereas leaving w1 and w2 by myself. comparability with Eigenvalues evaluate the formulation for eigenvectors and singular values: λ = E ± F 2 + G2 − H 2 w = E 2 + H 2 ± F 2 + G2 you will discover that, notwithstanding a matrix could or won't have actual eigenvalues, it usually has genuine singular values. The fabricated from the eigenvalues and of the singular values is usually an identical (it’s the determinant): λ1λ2 = w1w2 = E2 − F2 − G2 + H2 = ∆ provided that H = zero (symmetric matrix) are the eigenvalues and singular values an identical: λ1 = w1, λ2 = w2 precis of Invariants T precis of Invariants listed here are autonomous normal invariant amounts for a 2×2 matrix. for every of our 4 methods of expressing the matrix, they're A+D advert − BC E F 2 + G2 − H 2 λ1 λ2 Αw1 + w2Β cos Αγ + βΒ w1w2 plus any algebraic mixtures of them. 89 90 bankruptcy 5: reflect on the Lowly 2×2 Matrix There are 3 self reliant rotational invariants, the above plus yet another. With a few basic algebraic ﬁddling, the catalog may be extra simply written as A+D B−C advert − BC E H F2 + G2 λ1 λ2 attitude among eigenvectors w1 w2 γ+β plus any algebraic combos. discovering those invariants, in addition to fixing a number of different matrix difficulties, is simplest utilizing the E, F, G, H formula of the matrix parts. Visualizations Visualizations don’t find out about you, yet to me these kinds of formulation and houses are starting to merge right into a gentle, grey blur. Let’s try and make experience of this with a . . . watch for it . . . visualization. The A, B, C, D values of an arbitrary 2×2 matrix generate a 4D area. lets draw a diagram of this 4D house and divide it up into areas for many of the homes, yet visualizing in 4D is kinda tough. Let’s as an alternative see if we will be able to ﬁnd an affordable projection down into fewer dimensions. I’ve ﬂailed round with this for your time, and here’s my favourite projection.