2020
DOI: 10.1134/s0965542520020049
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Numerical Solution to a Control Problem for Integro-Differential Equations

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Cited by 33 publications
(15 citation statements)
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“…We use the approach offered in [16][17][18][19][20][21] to solve the boundary value problem ( 5), ( 6). This approach based on the algorithms of the parameterization method and numerical methods for solving Cauchy problems.…”
Section: Scheme Of Parametrization Methodmentioning
confidence: 99%
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“…We use the approach offered in [16][17][18][19][20][21] to solve the boundary value problem ( 5), ( 6). This approach based on the algorithms of the parameterization method and numerical methods for solving Cauchy problems.…”
Section: Scheme Of Parametrization Methodmentioning
confidence: 99%
“…It is not difficult to establish that the solvability of the boundary value problem ( 5), ( 6) is equivalent to the solvability of the system (20). The solution of the system (20) is a vector * * , * , … , * ∈ consists of the values of the solutions of the original problem (5), (6) We use the numerical implementation of algorithm.…”
Section: Scheme Of Parametrization Methodmentioning
confidence: 99%
“…In [4] we also propose a numerically approximate method for solving Cauchy problems for ordinary differential equations on subintervals, which is illustrated by numerical examples. The authors can present numerical results obtained using MathCad15.…”
Section: Algorithms For Solving a Problem With A Parameter For A Loadmentioning
confidence: 99%
“…Let the problem (5.1)-(5.2) be well-posed with the constant χ 0 . Then there exists a number h 1 > 0 such that for all h ∈ (0, h 1 ] : 2N h = T , the (n(2N + 1) + l) × (n(2N + 1) + l) matrix Q * (δ 2N (h)) is invertible, and the estimate[Q * (δ 2N (h))] −1 ≤ 2χ 0 /h (5.8)is true.The proofs of Theorems 11 and 12 with slight modifications are similar to the proofs of Theorems 4.1 and 4.2, respectively, in[4]. So, if problem (5.1)-(5.2) is well-posed with the constant χ 0 , then inequalities (5.7) and (5.8) yield the estimatecond ∞ Q * (δ 2N (h)) = Q * (δ 2N (h)) • [Q * (δ 2N (h))] −1 ≤2χ 0 h max h( B 0 + B + C ), 1 + (1 + βT h + α 0 h) exp(αh) (5.9)…”
mentioning
confidence: 93%
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