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Lecture #7 Stability and convergence of ODEs Hybrid Control and
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... The equilibrium point xeq ∈ Rn is (Lyapunov) stable if ∃ α ∈ 2: ||x(t) – xeq|| · α(||x(t0) – xeq||) ∀ t≥ t0≥ 0, ||x(t0) – xeq||· c Suppose we could show that ||x(t) – xeq|| always decreases along solutions to the ODE. Then ||x(t) – xeq|| · ||x(t0) – xeq|| ∀ t≥ t0≥ 0 we could pick α(s) = s ⇒ Lyapunov ...
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... The equilibrium point xeq 2 Rn is (Lyapunov) stable if 9 a 2 K: ||x(t) – xeq|| · a(||x(t0) – xeq||) 8 t¸ t0¸ 0, ||x(t0) – xeq||· c Theorem (Lyapunov): Suppose there exists a continuously differentiable, positive definite, radially unbounded function V: Rn ! R such that Then xeq is a Lyapunov stable ...
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... endowment destruction must not drop below some treshold ψ, which generally depends on the current strategy ω and the current state. Next, assume that γ < 1. In that case, the loss from driving ψ̃0 → −∞ is finite and given by (exp ((1 − γ)(sa0 + ψ0 + y0 )) − 1)/(1 − γ). However, the first term in the ...
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Generalized linear model

In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression. They proposed an iteratively reweighted least squares method for maximum likelihood estimation of the model parameters. Maximum-likelihood estimation remains popular and is the default method on many statistical computing packages. Other approaches, including Bayesian approaches and least squares fits to variance stabilized responses, have been developed.
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