By Robert W. Ghrist, Philip J. Holmes, Michael C. Sullivan (auth.)

The closed orbits of third-dimensional flows shape knots and hyperlinks. This booklet develops the instruments - template idea and symbolic dynamics - wanted for learning knotted orbits. This idea is utilized to the issues of knowing neighborhood and international bifurcations, in addition to the embedding facts of orbits in Morse-smale, Smale, and integrable Hamiltonian flows. The necesssary history thought is sketched; notwithstanding, a few familiarity with low-dimensional topology and differential equations is assumed.

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2] the vertices of P comprise a minimal set of generators. Given some arbitrary set C ⊆ Rn , its convex hull conv C is equivalent to the smallest convex set containing it. 1) The convex hull is a subset of the affine hull; P conv {xℓ , ℓ = 1 . . N } = conv X = {Xa | aT 1 = 1, a conv C ⊆ aff C = aff C = aff C = aff conv C (82) An arbitrary set C in Rn is bounded iff it can be contained in a Euclidean ball having finite radius. 16 x y∈C is a convex function of x ; but the supremum may be difficult to ascertain.

1 Example. Application of inverse image theorem. Suppose set C ⊆ Rp×k were convex. Then for any particular vectors v ∈ Rp and w ∈ Rk , the set of vector inner-products Y v TCw = vwT , C ⊆ R (38) is convex. 1. 11 Hadamard product is a simple entrywise product of corresponding entries from two matrices of like size; id est, not necessarily square. A commutative operation, the Hadamard product can be extracted from within a Kronecker product. 12 To verify that, take any two elements C1 and C2 from the convex matrix-valued set C , and then form the vector inner-products (38) that are two elements of Y by definition.

G. in place of the Latin exempli gratia. 1. 2 37 linear independence Arbitrary given vectors in Euclidean space {Γi ∈ Rn , i = 1 . . ) if and only if, for all ζ ∈ RN Γ1 ζ1 + · · · + ΓN −1 ζN −1 + ΓN ζN = 0 (5) has only the trivial solution ζ = 0 ; in other words, iff no vector from the given set can be expressed as a linear combination of those remaining. 1) Linear transformation preserves linear dependence. 86] Conversely, linear independence can be preserved under linear transformation. Given Y = [ y1 y2 · · · yN ] ∈ RN ×N , consider the mapping T (Γ) : Rn×N → Rn×N ΓY (6) whose domain is the set of all matrices Γ ∈ Rn×N holding a linearly independent set columnar.