Fixation (population genetics): Difference between revisions

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Also visit my website ... Hostgator Discount Voucher Template:Expand language Fourier–Motzkin elimination, also known as the FME method, is a mathematical algorithm for eliminating variables from a system of linear inequalities. It can look for both real and in some cases integer solutions.

The operation is named after Joseph Fourier and Theodore Motzkin.

Elimination

Elimination (or ∃-elimination) of variables V from a system of relations (here, linear inequalities) consists of creating another system of the same kind, but without a subset of variables V, such that both systems have the same solutions over the remaining variables.

If one eliminates all variables from a system of linear inequalities, then one obtains a system of constant inequalities, which can be trivially decided to be true or false, such that this system has solutions (is true) if and only if the original system has solutions. As a consequence, elimination of all variables can be used to detect whether a system of inequalities has solutions or not.

Consider a system $S$ of $n$ inequalities with $r$ variables $x_{1}$ to $x_{r}$ , with $x_{r}$ the variable to eliminate. The linear inequalities in the system can be grouped into three classes, depending on the sign (positive, negative or null) of the coefficient for $x_{r}$ :

those inequalities that are of the form $x_{r} \geq b_{i} - \sum_{k = 1}^{r - 1} a_{i k} x_{k}$ ; denote these by $x_{r} \geq A_{j} (x_{1}, \dots, x_{r - 1})$ , for $j$ ranging from 1 to $n_{A}$ where $n_{A}$ is the number of such inequalities;
those inequalities that are of the form $x_{r} \leq b_{i} - \sum_{k = 1}^{r - 1} a_{i k} x_{k}$ ; denote these by $x_{r} \leq B_{j} (x_{1}, \dots, x_{r - 1})$ , for $j$ ranging from 1 to $n_{B}$ where $n_{B}$ is the number of such inequalities;
those inequalities in which $x_{r}$ plays no role, grouped into a single conjunction $ϕ$ .

The original system is thus equivalent to $\max (A_{1} (x_{1}, \dots, x_{r - 1}), \dots, A_{n_{A}} (x_{1}, \dots, x_{r - 1})) \leq x_{r} \leq \min (B_{1} (x_{1}, \dots, x_{r - 1}), \dots, B_{n_{B}} (x_{1}, \dots, x_{r - 1})) \land ϕ$ .

Elimination consists in producing a system equivalent to $\exists x_{r} S$ . Obviously, this formula is equivalent to $\max (A_{1} (x_{1}, \dots, x_{r - 1}), \dots, A_{n_{A}} (x_{1}, \dots, x_{r - 1})) \leq \min (B_{1} (x_{1}, \dots, x_{r - 1}), \dots, B_{n_{B}} (x_{1}, \dots, x_{r - 1})) \land ϕ$ .

The inequality $\max (A_{1} (x_{1}, \dots, x_{r - 1}), \dots, A_{n_{A}} (x_{1}, \dots, x_{r - 1})) \leq \min (B_{1} (x_{1}, \dots, x_{r - 1}), \dots, B_{n_{B}} (x_{1}, \dots, x_{r - 1}))$ is equivalent to $n_{A} n_{B}$ inequalities $A_{i} (x_{1}, \dots, x_{r - 1}) \leq B_{j} (x_{1}, \dots, x_{r - 1})$ , for $1 \leq i \leq n_{A}$ and $1 \leq j \leq n_{B}$ .

We have therefore transformed the original system into another system where $x_{r}$ is eliminated. Note that the output system has $(n - n_{A} - n_{B}) + n_{A} n_{B}$ inequalities. In particular, if $n_{A} = n_{B} = n / 2$ , then the number of output inequalities is $n^{2} / 4$ .

Complexity

Running an elimination step over $n$ inequalities can result in at most $n^{2} / 4$ inequalities in the output, thus running $d$ successive steps can result in at most $4 (n / 4)^{2^{d}}$ , a double exponential complexity. This is due to the algorithm producing many unnecessary constraints (constraints that are implied by other constraints). The number of necessary constraints grows as a single exponential.^[1] Unnecessary constraints may be detected using linear programming.

References

↑ David Monniaux, Quantifier elimination by lazy model enumeration, Computer aided verification (CAV) 2010.

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20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

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External links

Lectures on Convex Sets, notes by Niels Lauritzen, at Aarhus University, March 2010.

Template:Optimization algorithms

[1] David Monniaux, Quantifier elimination by lazy model enumeration, Computer aided verification (CAV) 2010.

[1]

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+{{copy edit|for=proper encylopedic style of writing|date=September 2013}}
+{{expand language|topic=|langcode=de|otherarticle=|date=September 2013}}
+'''Fourier–Motzkin elimination''', also known as the '''FME method''', is a mathematical [[algorithm]] for eliminating variables from a [[system of linear inequalities]]. It can look for both [[real number|real]] and in some cases [[integer]] solutions.
+The operation is named after [[Joseph Fourier]] and [[Theodore Motzkin]].
+==Elimination==
+Elimination (or ∃-elimination) of variables ''V'' from a system of relations (here, linear inequalities) consists of creating another system of the same kind, but without a subset of variables ''V'', such that both systems have the same solutions over the remaining variables.
+If one eliminates all variables from a system of linear inequalities, then one obtains a system of constant inequalities, which can be trivially decided to be true or false, such that this system has solutions (is true) if and only if the original system has solutions. As a consequence, elimination of all variables can be used to detect whether a system of inequalities has solutions or not.
+Consider a system <math>S</math> of <math>n</math> inequalities with <math>r</math> variables <math>x_1</math> to <math>x_r</math>, with <math>x_r</math> the variable to eliminate. The linear inequalities in the system can be grouped into three classes, depending on the sign (positive, negative or null) of the coefficient for <math>x_r</math>:
+* those inequalities that are of the form <math>x_r \geq b_i-\sum_{k=1}^{r-1} a_{ik} x_k</math>; denote these by <math>x_r \geq A_j(x_1, \dots, x_{r-1})</math>, for <math>j</math> ranging from 1 to <math>n_A</math> where <math>n_A</math> is the number of such inequalities;
+* those inequalities that are of the form <math>x_r \leq b_i-\sum_{k=1}^{r-1} a_{ik} x_k</math>; denote these by <math>x_r \leq B_j(x_1, \dots, x_{r-1})</math>, for <math>j</math> ranging from 1 to <math>n_B</math> where <math>n_B</math> is the number of such inequalities;
+* those inequalities in which <math>x_r</math> plays no role, grouped into a single conjunction <math>\phi</math>.
+The original system is thus equivalent to <math>\max(A_1(x_1, \dots, x_{r-1}), \dots, A_{n_A}(x_1, \dots, x_{r-1})) \leq x_r \leq \min(B_1(x_1, \dots, x_{r-1}), \dots, B_{n_B}(x_1, \dots, x_{r-1})) \wedge \phi</math>.
+Elimination consists in producing a system equivalent to <math>\exists x_r~S</math>. Obviously, this formula is equivalent to <math>\max(A_1(x_1, \dots, x_{r-1}), \dots, A_{n_A}(x_1, \dots, x_{r-1})) \leq \min(B_1(x_1, \dots, x_{r-1}), \dots, B_{n_B}(x_1, \dots, x_{r-1})) \wedge \phi</math>.
+The inequality <math>\max(A_1(x_1, \dots, x_{r-1}), \dots, A_{n_A}(x_1, \dots, x_{r-1})) \leq \min(B_1(x_1, \dots, x_{r-1}), \dots, B_{n_B}(x_1, \dots, x_{r-1}))</math> is equivalent to <math>n_A n_B</math> inequalities <math>A_i(x_1, \dots, x_{r-1}) \leq B_j(x_1, \dots, x_{r-1})</math>, for <math>1 \leq i \leq n_A</math> and <math>1 \leq j \leq n_B</math>.
+We have therefore transformed the original system into another system where <math>x_r</math> is eliminated. Note that the output system has <math>(n-n_A-n_B)+n_A n_B</math> inequalities. In particular, if <math>n_A = n_B = n/2</math>, then the number of output inequalities is <math>n^2/4</math>.
+==Complexity==
+Running an elimination step over <math>n</math> inequalities can result in at most <math>n^2/4</math> inequalities in the output, thus running <math>d</math> successive steps can result in at most <math>4(n/4)^{2^d}</math>, a double exponential complexity.
+This is due to the algorithm producing many unnecessary constraints (constraints that are implied by other constraints). The number of necessary constraints grows as a single exponential.<ref>David Monniaux, [http://hal.archives-ouvertes.fr/hal-00472831 ''Quantifier elimination by lazy model enumeration''], Computer aided verification (CAV) 2010.</ref>
+Unnecessary constraints may be detected using [[linear programming]].
+==See also==
+* [[Real closed field]]: the cylindrical algebraic decomposition algorithm performs quantifier elimination over ''polynomial'' inequalities, not just linear.
+== References ==
+<references/>
+*{{Cite book|last=Fourier|first=Joseph|authorlink=Joseph Fourier|chapter=Histoire de l'Académie, partie mathématique (1824)|title=Mémoires de l'Académie des sciences de l'Institut de France|volume=7|publisher=Gauthier-Villars|year=1827|url=http://gallica.bnf.fr/ark:/12148/bpt6k32227/f53}}
+*{{Cite book |last=Schrijver |first=Alexander|authorlink=Alexander Schrijver |title=Theory of Linear and Integer Programming |location= |publisher=John Wiley & sons |year=1998 |isbn=0-471-98232-6 |pages=155–156 }}
+*{{Cite web |last=Keßler |first=Christoph W. |title=Parallel Fourier–Motzkin Elimination |work=Universität Trier |id = {{citeseerx|10.1.1.54.657}} }}
+*{{Cite journal |last=Williams |first=H. P. |title=Fourier's Method of Linear Programming and its Dual |journal=American Mathematical Monthly |volume=93 |year=1986 |issue= 9|pages=681–695 |doi=10.2307/2322281 }}
+== External links ==
+* [http://www.fmf.uni-lj.si/~lavric/lauritzen.pdf Lectures on Convex Sets], notes by Niels Lauritzen, at [[Aarhus University]], March 2010.
+{{Optimization algorithms}}
+{{DEFAULTSORT:Fourier-Motzkin elimination}}
+[[Category:Optimization algorithms and methods]]
+[[Category:Real algebraic geometry]]

Fixation (population genetics): Difference between revisions

Revision as of 18:11, 24 December 2013

Contents

Elimination

Complexity

See also

References

External links

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Fixation (population genetics): Difference between revisions

Revision as of 18:11, 24 December 2013

Elimination

Complexity

See also

References

External links

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