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author | Prefetch | 2021-02-25 16:14:20 +0100 |
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committer | Prefetch | 2021-02-25 16:14:20 +0100 |
commit | c705ac1d7dc74709835a8c48fae4a7dd70dc5c49 (patch) | |
tree | 5f618f86bd96649c796a5ccf4bf1542046a02561 /content/know/concept/calculus-of-variations/index.pdc | |
parent | c157ad913aa9f975ea8c137e24175d134486f462 (diff) |
Expand knowledge base
Diffstat (limited to 'content/know/concept/calculus-of-variations/index.pdc')
-rw-r--r-- | content/know/concept/calculus-of-variations/index.pdc | 40 |
1 files changed, 20 insertions, 20 deletions
diff --git a/content/know/concept/calculus-of-variations/index.pdc b/content/know/concept/calculus-of-variations/index.pdc index fb043e0..c5280e5 100644 --- a/content/know/concept/calculus-of-variations/index.pdc +++ b/content/know/concept/calculus-of-variations/index.pdc @@ -29,18 +29,18 @@ the path $f(x)$ taken by a physical system, the **principle of least action** states that $f$ will be a minimum of $J[f]$, so for example the expended energy will be minimized. -If $f(x, \alpha\!=\!0)$ is the optimal route, then a slightly +If $f(x, \varepsilon\!=\!0)$ is the optimal route, then a slightly different (and therefore worse) path between the same two points can be expressed -using the parameter $\alpha$: +using the parameter $\varepsilon$: $$\begin{aligned} - f(x, \alpha) = f(x, 0) + \alpha \eta(x) + f(x, \varepsilon) = f(x, 0) + \varepsilon \eta(x) \qquad \mathrm{or} \qquad - \delta f = \alpha \eta(x) + \delta f = \varepsilon \eta(x) \end{aligned}$$ Where $\eta(x)$ is an arbitrary differentiable deviation. -Since $f(x, \alpha)$ must start and end in the same points as $f(x,0)$, +Since $f(x, \varepsilon)$ must start and end in the same points as $f(x,0)$, we have the boundary conditions: $$\begin{aligned} @@ -50,16 +50,16 @@ $$\begin{aligned} Given $L$, the goal is to find an equation for the optimal path $f(x,0)$. Just like when finding the minimum of a real function, the minimum $f$ of a functional $J[f]$ is a stationary point -with respect to the deviation weight $\alpha$, +with respect to the deviation weight $\varepsilon$, a condition often written as $\delta J = 0$. In the following, the integration limits have been omitted: $$\begin{aligned} 0 &= \delta J - = \pdv{J}{\alpha} \Big|_{\alpha = 0} - = \int \pdv{L}{\alpha} \dd{x} - = \int \pdv{L}{f} \pdv{f}{\alpha} + \pdv{L}{f'} \pdv{f'}{\alpha} \dd{x} + = \pdv{J}{\varepsilon} \Big|_{\varepsilon = 0} + = \int \pdv{L}{\varepsilon} \dd{x} + = \int \pdv{L}{f} \pdv{f}{\varepsilon} + \pdv{L}{f'} \pdv{f'}{\varepsilon} \dd{x} \\ &= \int \pdv{L}{f} \eta + \pdv{L}{f'} \eta' \dd{x} = \Big[ \pdv{L}{f'} \eta \Big]_{x_0}^{x_1} + \int \pdv{L}{f} \eta - \frac{d}{dx} \Big( \pdv{L}{f'} \Big) \eta \dd{x} @@ -99,16 +99,16 @@ In this case, every $f_n(x)$ has its own deviation $\eta_n(x)$, satisfying $\eta_n(x_0) = \eta_n(x_1) = 0$: $$\begin{aligned} - f_n(x, \alpha) = f_n(x, 0) + \alpha \eta_n(x) + f_n(x, \varepsilon) = f_n(x, 0) + \varepsilon \eta_n(x) \end{aligned}$$ The derivation procedure is identical to the case $N = 1$ from earlier: $$\begin{aligned} 0 - &= \pdv{J}{\alpha} \Big|_{\alpha = 0} - = \int \pdv{L}{\alpha} \dd{x} - = \int \sum_{n} \Big( \pdv{L}{f_n} \pdv{f_n}{\alpha} + \pdv{L}{f_n'} \pdv{f_n'}{\alpha} \Big) \dd{x} + &= \pdv{J}{\varepsilon} \Big|_{\varepsilon = 0} + = \int \pdv{L}{\varepsilon} \dd{x} + = \int \sum_{n} \Big( \pdv{L}{f_n} \pdv{f_n}{\varepsilon} + \pdv{L}{f_n'} \pdv{f_n'}{\varepsilon} \Big) \dd{x} \\ &= \int \sum_{n} \Big( \pdv{L}{f_n} \eta_n + \pdv{L}{f_n'} \eta_n' \Big) \dd{x} \\ @@ -140,9 +140,9 @@ Once again, the derivation procedure is the same as before: $$\begin{aligned} 0 - &= \pdv{J}{\alpha} \Big|_{\alpha = 0} - = \int \pdv{L}{\alpha} \dd{x} - = \int \pdv{L}{f} \pdv{f}{\alpha} + \sum_{n} \pdv{L}{f^{(n)}} \pdv{f^{(n)}}{\alpha} \dd{x} + &= \pdv{J}{\varepsilon} \Big|_{\varepsilon = 0} + = \int \pdv{L}{\varepsilon} \dd{x} + = \int \pdv{L}{f} \pdv{f}{\varepsilon} + \sum_{n} \pdv{L}{f^{(n)}} \pdv{f^{(n)}}{\varepsilon} \dd{x} \\ &= \int \pdv{L}{f} \eta + \sum_{n} \pdv{L}{f^{(n)}} \eta^{(n)} \dd{x} \end{aligned}$$ @@ -187,17 +187,17 @@ $$\begin{aligned} The arbitrary deviation $\eta$ is then also a function of multiple variables: $$\begin{aligned} - f(x, y; \alpha) = f(x, y; 0) + \alpha \eta(x, y) + f(x, y; \varepsilon) = f(x, y; 0) + \varepsilon \eta(x, y) \end{aligned}$$ The derivation procedure starts in the exact same way as before: $$\begin{aligned} 0 - &= \pdv{J}{\alpha} \Big|_{\alpha = 0} - = \iint \pdv{L}{\alpha} \dd{x} \dd{y} + &= \pdv{J}{\varepsilon} \Big|_{\varepsilon = 0} + = \iint \pdv{L}{\varepsilon} \dd{x} \dd{y} \\ - &= \iint \pdv{L}{f} \pdv{f}{\alpha} + \pdv{L}{f_x} \pdv{f_x}{\alpha} + \pdv{L}{f_y} \pdv{f_y}{\alpha} \dd{x} \dd{y} + &= \iint \pdv{L}{f} \pdv{f}{\varepsilon} + \pdv{L}{f_x} \pdv{f_x}{\varepsilon} + \pdv{L}{f_y} \pdv{f_y}{\varepsilon} \dd{x} \dd{y} \\ &= \iint \pdv{L}{f} \eta + \pdv{L}{f_x} \eta_x + \pdv{L}{f_y} \eta_y \dd{x} \dd{y} \end{aligned}$$ |