Results 151 to 160 of about 522,371 (218)

Algorithm for Decomposition of Integers and Smooth Approximation of Functions

Mathematical and computer modelling. Series: Physical and mathematical sciences, 2022
Узагальнено задачу про розклад степенів на розклад цілих додатних чисел за послідовністю степенів різних порядків, виведені умови розкладу, побудовано алгоритм розкладу. Алгоритм заснований на двох процедурах: 1) досягнення мінімуму нев’язки на кожному кроці алгоритму, 2) прискорення швидкості розкладу шляхом розширення локального базису за рахунок ...
Vasyl Abramchuk, Ihor Ihor Abramchuk
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RONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure

arXiv.org, 2023
Many engineering applications rely on the evaluation of expensive, non-linear high-dimensional functions. In this paper, we propose the RONAALP algorithm (Reduced Order Nonlinear Approximation with Active Learning Procedure) to incrementally learn a fast
C. Scherding, Georgios Rigas, D. Sipp, Peter J. Schmid, Taraneh Sayadi Institut Jean le Rond d'Alembert, Sorbonne University, Department of Aeronautics, I. -. London, Daaa, Onera, Department of Mechanical Engineering, Kaust, Institute for Energy Technology, A. University   +13 more
semanticscholar   +1 more source

Best Algorithms for Approximating the Maximum of a Submodular Set Function

Mathematics of Operations Research, 1978
A real-valued function z whose domain is all of the subsets of N = {1, …, n) is said to be submodular if z(S) + z(T) ≥ z(S ∪ T) + z(S ∩ T), ∀S, T ⊆ N, and nondecreasing if z(S) ≤ z(T), ∀S ⊂ T ⊆ N. We consider the problem maxS⊂N {z(S): |S| ≤ K, z submodular and nondecreasing, z(Ø) = 0}.
Nemhauser, G. L., Wolsey, L. A.
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A Multi-Table Approach to Floating-Point Function Approximation

Conference of the Centre for Advanced Studies on Collaborative Research
AI, analytics and database performance depend on two types of hot functions: linear algebra, and non-linear functions. In this paper, we describe a novel method of accelerating the approximation of non-linear functions, with workedout examples for divide,
Lucas M. Dutton, C. K. Anand, R. Enenkel, Silvia M. Müller   +3 more
semanticscholar   +1 more source

High Accuracy Algorithms for Approximation of Discontinuity Lines of a Noisy Function

Proceedings of the Steklov Institute of Mathematics, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ageev, A. L., Antonova, T. V.
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Advancing Computational Accuracy and Efficiency through Machine Learning in Numerical Analysis

Journal of Kufa for Mathematics and Computer
The power from machine learning based solution strategies is shown by using them for hybridizing computational models with data-driven exploratory features. The findings provide theoretical underpinnings and algorithms for practical computational methods
Alaa Abbas Habib Aboub Abbas Habib
semanticscholar   +1 more source

Local Function Approximation in Evolutionary Algorithms for the Optimization of Costly Functions

IEEE Transactions on Evolutionary Computation, 2004
We develop an approach for the optimization of continuous costly functions that uses a space-filling experimental design and local function approximation to reduce the number of function evaluations in an evolutionary algorithm. Our approach is to estimate the objective function value of an offspring by fitting a function approximation model over the k
R.G. Regis, C.A. Shoemaker
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An optimal adaptive algorithm for the approximation of concave functions

Mathematical Programming, 2005
Consider a proper concave function \(f:[ 0,1] \to\mathbb R\), normalized so that \(f( 0) =0\) and \(f( 1) =1.\) \ Denote by \(f^{\prime }( \overline{x}) \) an arbitrary supergradient \(\xi \) of \(f\) at \(\overline{x},\) i.e., a supergradient \(\xi \) satisfying: \[ f( x) \leq f( \overline{x}) +\xi ( x-\overline{x} ) \text{ for all }x\in [ 0,1] \] Let
Guérin, J., Marcotte, P., Savard, G.
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ALGORITHM FOR CALCULATING FUNCTIONS IN METHOD OF SUCCESSIVE APPROXIMATIONS

International Journal of Modern Physics B, 1989
A new algorithm is proposed for constructing unknown functions with the help of their several approximate expressions. The algorithm is illustrated by calculating the ground-state energy of anharmonic oscillator. Different variants of the method are analysed, and it is shown that the accuracy of the calculations can be carried to 0.1%.
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