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Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions
TLDR
The Nelder--Mead simplex algorithm, first published in 1965, is an enormously popular direct search method for multidimensional unconstrained minimization . Expand
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OPTIMAL PATHS FOR A CAR THAT GOES BOTH FORWARDS AND BACKWARDS
The path taken by a car with a given minimum turning radius has a lower bound on its radius of curvature at each point, but the path has cusps if the car shifts into or out of reverse gear. What isExpand
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Estimating Solutions of First Kind Integral Equations with Nonnegative Constraints and Optimal Smoothing
A method is presented for estimating solutions of Fredholm integral equations of the first kind, given noisy data. Regularization is effected by a smoothing term which is the $L^2 $-norm of theExpand
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Shift-Register Synthesis (Modulo m)
TLDR
The Berlekamp-Massey algorithm takes a sequence of elements from a field and finds the shortest linear recurrence (or linear feedback shift register) that can generate the sequence. Expand
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Inequalities and Positive-Definite Functions Arising from a Problem in Multidimensional Scaling
We solve the following variational problem: Find the maximum of E ∥ X−Y ∥ subject to E ∥ X ∥2 ≤ 1, where X and Y are i.i.d. random n-vectors, and ∥⋅∥ is the usual Euclidean norm on Rn. This problemExpand
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Orthonormal bases of exponentials for the n-cube
Any set that gives such an orthogonal basis is called a spectrum for . Only very special sets in R are spectral sets. However, when a spectrum exists, it can be viewed as a generalization of FourierExpand
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Identification of stops and vowels for the burst portion of (p,t,k) isolated from conversational speech.
The stops /p t k/ were tested for identification in the context of /i a u/. The burst portions of initial and final stops were isolated from conversational speech through electronic gating circuitry.Expand
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Sets Uniquely Determined by Projections on Axes I
This paper studies sets S in $\mathbb{R}''$ which are uniquely reconstructible from their hyperplane integral projections $P_i ( {x_i ;S} ) = \iint { \cdots \int {\chi _S } }( {x_1 , \cdots ,x_i ,Expand
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"Cracking" a Random Number Generator
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