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[Analysis] Final tweaks
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@@ -67,3 +67,5 @@ where the lowest value is the global minimum and the highest value the global ma
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Always consider the corners as possible maxima or minima (if some corners are critical points, all are highly likely to be).
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Always consider the corners as possible maxima or minima (if some corners are critical points, all are highly likely to be).
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The tangent plane at a critical point of a function $f : \R^n \rightarrow \R$, is of the form $\{ (x, y, z) \divides z = \text{const} \}$, with $z = f(x_0)$.
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The tangent plane at a critical point of a function $f : \R^n \rightarrow \R$, is of the form $\{ (x, y, z) \divides z = \text{const} \}$, with $z = f(x_0)$.
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Note that a global minimum or maximum is the maximal / minimal element of the range of the function.
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@@ -43,6 +43,7 @@ If any two points of $X$ can be joined by a parametrized curve, then $g$ is uniq
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\shortremark Two points $x, y \in X$ can be joined by parametrized curve $\gamma$ if $\gamma(a) = x$ and $\gamma(b) = y$. In that case, $X$ is called \bi{path-connected}.
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\shortremark Two points $x, y \in X$ can be joined by parametrized curve $\gamma$ if $\gamma(a) = x$ and $\gamma(b) = y$. In that case, $X$ is called \bi{path-connected}.
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It is true when $X$ is \textit{convex} (e.g. when $X$ is a disc or a product of intervals).
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It is true when $X$ is \textit{convex} (e.g. when $X$ is a disc or a product of intervals).
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If $f$ is a vector field on $X$, then $g$ is called a \bi{potential} for $f$ and it is not unique, since we can add a constant to $g$ without changing the gradient.\\
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If $f$ is a vector field on $X$, then $g$ is called a \bi{potential} for $f$ and it is not unique, since we can add a constant to $g$ without changing the gradient.\\
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\shade{gray}{Finding a potential} We want a $g$ s.t. $\nabla g = f$ for some conservative $f$, so we find an anti-derivative $g$ for which $\nabla g = f$\\
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%
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%
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\stepLabelNumber{all}
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\stepLabelNumber{all}
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\shortproposition For a vectorfield to be conservative, a \textit{necessary condition} is that $\displaystyle\frac{\partial f_i}{\partial x_j} = \frac{\partial f_j}{x_i}$
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\shortproposition For a vectorfield to be conservative, a \textit{necessary condition} is that $\displaystyle\frac{\partial f_i}{\partial x_j} = \frac{\partial f_j}{x_i}$
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