Fifth Property of the Euclidean Metric

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For a list of points <math>\{x_\ell\in\mathbb{R}^n,\,\ell\!=\!1\ldots N\}</math> in Euclidean vector space, distance-square between points <math>x_i</math> and <math>x_j</math> is defined:
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For a list of points <math>\{x_\ell\in\mathbb{R}^n,\,\ell\!=\!1\ldots N\}</math> in Euclidean vector space, distance-square between points <math>x_i</math> and <math>x_j</math> is defined
<math>\begin{array}{rl}d_{ij}
<math>\begin{array}{rl}d_{ij}
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\end{array}</math>
\end{array}</math>
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[[Euclidean distance]] must satisfy the requirements imposed by any metric space.
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[[Euclidean distance]] must satisfy the requirements imposed by any metric space:
{{harvtxt|Dattorro|2007, ch.5.2}}
{{harvtxt|Dattorro|2007, ch.5.2}}
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* <math>\sqrt{d_{ij}}\,\leq\,\sqrt{d_{ik_{}}}+\sqrt{d_{kj}}~,~~i\!\neq\!j\!\neq\!k</math> '''('''triangle inequality''')'''
* <math>\sqrt{d_{ij}}\,\leq\,\sqrt{d_{ik_{}}}+\sqrt{d_{kj}}~,~~i\!\neq\!j\!\neq\!k</math> '''('''triangle inequality''')'''
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where <math>\sqrt{d_{ij}}</math> is the Euclidean metric in <math>\mathbb{R}^n</math>
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where <math>\sqrt{d_{ij}}</math> is the Euclidean metric in <math>\mathbb{R}^n</math>.
==Fifth property of the Euclidean metric ==
==Fifth property of the Euclidean metric ==
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\end{array}</math>
\end{array}</math>
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where <math>\theta_{ikj}\!=_{}\!\theta_{jki}</math> is the angle between vectors at vertex <math>x_k</math> must be satisfied at each point <math>x_k</math> regardless of affine dimension.
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where <math>\theta_{ikj}\!=_{}\!\theta_{jki}</math> is the angle between vectors at vertex <math>x_k</math>, must be satisfied at each point <math>x_k</math> regardless of affine dimension.
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== References ==
== References ==
* {{citation | first1 = Jon | last1 = Dattorro | year = 2007 | title = Convex Optimization & Euclidean Distance Geometry | url = http://www.convexoptimization.com | publisher = Meboo | isbn = 0976401304 }}.
* {{citation | first1 = Jon | last1 = Dattorro | year = 2007 | title = Convex Optimization & Euclidean Distance Geometry | url = http://www.convexoptimization.com | publisher = Meboo | isbn = 0976401304 }}.

Revision as of 20:54, 30 October 2007

For a list of points LaTeX: \{x_\ell\in\mathbb{R}^n,\,\ell\!=\!1\ldots N\} in Euclidean vector space, distance-square between points LaTeX: x_i and LaTeX: x_j is defined

LaTeX: \begin{array}{rl}d_{ij} \!\!&=\,\|x_i-_{}x_j\|^2 ~=~(x_i-_{}x_j)^T(x_i-_{}x_j)~=~\|x_i\|^2+\|x_j\|^2-2_{}x^T_i\!x_j\\\\ &=\,\left[x_i^T\quad x_j^T\right]\left[\begin{array}{*{20}r}\!I&-I\\\!-I&I\end{array}\right] \left[\!\!\begin{array}{*{20}c}x_i\\x_j\end{array}\!\!\right] \end{array}

Euclidean distance must satisfy the requirements imposed by any metric space:

Template:Harvtxt

  • LaTeX: \sqrt{d_{ij}}\geq0\,,~~i\neq j (nonnegativity)
  • LaTeX: \sqrt{d_{ij}}=0\,,~~i=j (self-distance)
  • LaTeX: \sqrt{d_{ij}}=\sqrt{d_{ji}} (symmetry)
  • LaTeX: \sqrt{d_{ij}}\,\leq\,\sqrt{d_{ik_{}}}+\sqrt{d_{kj}}~,~~i\!\neq\!j\!\neq\!k (triangle inequality)

where LaTeX: \sqrt{d_{ij}} is the Euclidean metric in LaTeX: \mathbb{R}^n.

Fifth property of the Euclidean metric

(Relative-angle inequality.)

Augmenting the four fundamental Euclidean metric properties in LaTeX: \mathbb{R}^n, for all LaTeX: i_{},j_{},\ell\neq k_{}\!\in\!\{1\ldots_{}N\}_{\,}, LaTeX: i\!<\!j\!<\!\ell\,\,, and for LaTeX: N\!\geq_{\!}4 distinct points LaTeX: \{x_k\}_{\,}, the inequalities

LaTeX: \begin{array}{cc} |\theta_{ik\ell}-\theta_{\ell kj}|~\leq~\theta_{ikj\!}~\leq~\theta_{ik\ell}+\theta_{\ell kj}\quad\quad&{\rm(a)}\\ \theta_{ik\ell}+\theta_{\ell kj}+\theta_{ikj\!}\,\leq\,2\pi\quad\quad&{\rm(b)}\\ 0\leq\theta_{ik\ell\,},\theta_{\ell kj\,},\theta_{ikj}\leq\pi\quad\quad&{\rm(c)} \end{array}

where LaTeX: \theta_{ikj}\!=_{}\!\theta_{jki} is the angle between vectors at vertex LaTeX: x_k, must be satisfied at each point LaTeX: x_k regardless of affine dimension.


References

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