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Error Bounds for Quadratic Systems
2000In this paper we consider the problem of estimating the distance from a given point to the solution set of a quadratic inequality system. We show, among other things, that a local error bound of order 1/2 holds for a system defined by linear inequalities and a single (nonconvex) quadratic equality.
Luo, Z-Q, Sturm, J.F.
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Bounded error adaptive control
IEEE Transactions on Automatic Control, 1980Identification and control when plant output is corrupted with disturbance and when plant parameters vary with time are considered. It is shown how the error model can be described by a non-homogeneous differential equation. Subject to bounded disturbance and parameter variation and sufficiently rich input the parameter error vector remains bounded and
Peterson, Benjamin B. +1 more
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Asymptotic Approximations and Error Bounds
SIAM Review, 1980The purpose of this paper is to demonstrate that well-constructed error bounds for asymptotic approximations can provide useful analytical insight into the nature and reliability of the approximati...
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New Uniform Parametric Error Bounds
Journal of Optimization Theory and Applications, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Upper Error Bound Selection in Bounded Parameter Estimation
IFAC Proceedings Volumes, 1994Abstract The problem of upper error bound selection in bounded parameter estimation for models linear-in-the-parameters is considered. It is shown that this selection implies a trade-off between posterior parametric and nonparametric uncertainty. A minimum uncertainty description of the model behaviour is obtained from min-max parameter estimation ...
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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
We present a feedback motion planning algorithm, Bounded-Error LQR-Trees, that leverages reinforcement learning theory to find a policy with a bounded amount of error. The algorithm composes locally valid linear-quadratic regulators (LQR) into a nonlinear controller, similar to how LQR-Trees constructs its policy, but minimizes the cost of the ...
Barrett Ames, George Konidaris
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We present a feedback motion planning algorithm, Bounded-Error LQR-Trees, that leverages reinforcement learning theory to find a policy with a bounded amount of error. The algorithm composes locally valid linear-quadratic regulators (LQR) into a nonlinear controller, similar to how LQR-Trees constructs its policy, but minimizes the cost of the ...
Barrett Ames, George Konidaris
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Computing in Science & Engineering, 2021
Prompted by previous work published in this magazine, in this article we focus on the derivation of global analytical bounds for the error function of a real argument. Using an integral representation of this function, we obtain two simple and accurate lower bounds, which complement a well-known upper bound given long ago by Polya.
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Prompted by previous work published in this magazine, in this article we focus on the derivation of global analytical bounds for the error function of a real argument. Using an integral representation of this function, we obtain two simple and accurate lower bounds, which complement a well-known upper bound given long ago by Polya.
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Bounds and error bounds for queueing networks
Annals of Operations Research, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Bounded‐error estimation using dead zone and bounding ellipsoid
International Journal of Adaptive Control and Signal Processing, 1994AbstractThe use of a dead zone and a bounding ellipsoid for parameter estimation when measurement errors are bounded is discussed. the size of the dead zone is set to be exactly equal to the assumed noise bound. The algorithm retains the properties of computing parameter point estimates and allows a bounding ellipsoid to be computed at each iterative ...
Evans, R. J., Zhang, C., Soh, Y. C.
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Naval Research Logistics, 1988
We explore the properties of the discounted total cost function for the economic order quantity. We show that it is convex. Furthermore, it is shown that the classical economic order quantity (based on Wilson's formula) is not less than the true optimum value based on discounting.
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We explore the properties of the discounted total cost function for the economic order quantity. We show that it is convex. Furthermore, it is shown that the classical economic order quantity (based on Wilson's formula) is not less than the true optimum value based on discounting.
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