We present Mn K-edge XANES data for a number of manganite systems as a function of temperature. The main edge (1s-4p transition) for the Ca substituted samples is very sharp, almost featureless, and shifts uniformly upwards with increasing Ca content. The interpretation of this result

minimize f0(x) subject to fi (x) ≤ 0, i = 1,...,m Ax = b ◮ variable x ∈ Rn ◮ f0,..., fm are convex: for θ ∈ [0, 1], fi (θx + (1 − θ)y) ≤ θfi (x) + (1 − θ)fi (y) i.e., fi have nonnegative (upward) curvature Convex Optimization 4 Convex optimization — Cone form minimize cT x subject to x ∈ K Ax = b

is the value that d2 associates with the profile of the top-ranked alternatives in P restricted to A\{x1}. The third alternative in p is the value that d3 associates with the profile of the top-ranked alternatives in P restricted to A\{x1, x2}. And so on until the m positions are filled. The upward procedure

We investigate the generation of intrinsically asymmetric or one-sided outflows or jets from disk accretion onto rotating stars with complex magnetic fields using ax-isymmetric (2.5D) magnetohydrodynamic simulations. The intrinsic magnetic field of the star is assumed to consist of a superposition

= b i = 1,..., m • objective and inequality constraint functions fi are convex: for all x, y, θ ∈ [0,1], fi(θx + (1 − θ)y) ≤ θfi(x) + (1 − θ)fi(y) roughly speaking, graphs of fi curve upward • equality constraint functions are affine, so can be written as Ax = b UW, 4/5/08 3Convex optimization • a

Lisp"). It is intended to be a modernization of the programming language LISP suitable in design for implementation on any of the current generation of large-address-space, low-cost computers; and maximally upward-compatible with MACLlSP, the dialect of LISP developed at the M.I.T Laboratory

disjoint NP sets A(x, y) and B(x, z), where furthermore the set A is upwards-closed and the set B is downwards-closed. For example, x may code the vertices of a graph, y may code a k-clique, and z may code a k − 1-coloring of the vertices of the graph. The formula A(x, y) states that y represents a clique

to cover an ofice building. Alternatively, transmitter powers could be raised to compensate for this, but this may then greatly increase the size of the personal terminal, or require more frequent recharging of batteries. The move upwards in frequency will therefore impact system costs, unless technology

Convex optimization problem — standard form minimize f0(x) subject to fi(x) ≤ 0, i = 1,...,m Ax = b with variable x ∈ Rn I objective and inequality constraints f0,..., fm are convex for all x, y, θ ∈ [0, 1], fi(θx + (1 − θ)y) ≤ θfi(x) + (1 − θ)fi(y) i.e., graphs of fi curve upward I equality

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