CHEMICAL REACTIONS & REACTIONS &BALANCINGEQUATIONSChemistry.
Malta.html Reactions, mechanisms and simplexesszalkai/Eload19-Malta.pdf · 1 Reactions, mechanisms...
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Reactions, mechanisms and
simplexes
István Szalkai
Pannon University, Veszprém, Hungary
Department of Mathematics
http://math.uni-pannon.hu/~szalkai/Malta.html
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http://math.uni-pannon.hu/~szalkai/Malta.html
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I.
Simplexes(definitions)
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2 H2 + 2 CO = CH4 + CO2
H: |2| |0| |4| |0| |0|
C: 2*|0| + 2*|1| - |1| - |1| = |0|
O: |0| |1| |0| |2| |0|
(1) Chemical reactions :
<=> Linear combination of vectors
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2 H2 + 2 CO = CH4 + CO2
H: |2| |0| |4| |0| |0|
C: 2*|0| + 2*|1| - |1| - |1| = |0|
O: |0| |1| |0| |2| |0|
(1) Chemical reactions :
<=> Linear combination of vectors
No kinetics, chemics, graphs (at the end),
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other approaches: / [email protected] /
Tóth,J., Érdi,P.: Mathematical Models of Chemical Reactions.
Theory and Applications of Deterministic and Stochastic Models,
Manchester Univ. Press and Princeton Univ.Press, 1989.
Tóth,J., Li,G., Rabitz,H., Tomlin,A.S.: The Effect of Lump-
ing and Expanding on Kinetic Differential Equations, SIAM J.
Appl. Math., 57 (1997), 1531-1556.
Tóth,J., Nagy,A.L., Zsély,I.: Structural Analysis of Combus-
tion Models, arXiv preprint arXiv:1304.7964 (2013).
Tóth,J., Rospars,J.P.: Dynamic Modeling of Biochemical Reac-
tions with Applications to Signal Transduction: Principles and
Tools using Mathematica, Biosystems 79 (1-3), (2005) 33-52.
Tóth,J., Nagy,A., Papp,D.: Reaction Kinetics: Exercises, Prog-
rams and Theorems: Mathematica for Deterministic and Stochas-
tic Kinetics, Springer, New York, NY, 2018.
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2 H2 + 2 CO = CH4 + CO2
H: |2| |0| |4| |0| |0|
C: 2*|0| + 2*|1| - |1| - |1| = |0|
O: |0| |1| |0| |2| |0|
(1) Chemical reactions :
<=> Linear combination of vectors
Minimal: none of them can be omitted.
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2 H2 + 2 CO = CH4 + CO2
H: |2| |0| |4| |0| |0|
C: 2*|0| + 2*|1| - |1| - |1| = |0|
O: |0| |1| |0| |2| |0|
(1) Chemical reactions :
<=> Linear combination of vectors
Minimal: none of them can be omitted.
(also for ions, e-, cathalysts, etc.)
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2 H2 + 2 CO = CH4 + CO2
H: |2| |0| |4| |0| |0|
C: 2*|0| + 2*|1| - |1| - |1| = |0|
O: |0| |1| |0| |2| |0|
(1) Chemical reactions :
<=> Linear combination of vectors
Minimal: none of them can be omitted.
(also for ions, e-, cathalysts, etc.)
RC,O,H,e-,...
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Question:
Can we distinguish the reactions u: X+Y2X and v: YX ?
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Question:
Can we distinguish the reactions u: X+Y2X and v: YX ?
Answer: The above model does not distinguish them: reduces to 0
and uses the vector w = [1,-1,0,...]T for both.
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Question:
Can we distinguish the reactions u: X+Y2X and v: YX ?
Answer: The above model does not distinguish them: reduces to 0
and uses the vector w = [1,-1,0,...]T for both.
Idea: work in double dimension. Imagine for all species (X,Y, ...)
two variants "in" and "out" and use the vectors:
u' = [-1,-1,0,...,2,0,0,...]T , v': [0,-1,0,...,1,0,0,...]T ,
and introduce the reactions "in out" as:
x = [1,0,0, ..., -1,0,0,...]T , y = [0,1,0, ...,0,-1,0,...]T ,
then clearly u u'+2x and v v' + x ,
and modify the original "start" and "goal" reactions corres-
ponding this idea.
v' + x
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. . . there are several more minor observations and tricks . . .
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1: C + O = CO X1 = [1,1,-1,0]
2: C + 2 O = CO2 X2 = [1,2,0,-1]
( 3: O + CO = CO2 X3 = [0,1,1,-1] )
4: C + CO2= 2CO X4 = [1,0,-2,1]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2*X1 - X2 = X4
2*X1 - X2 - X4 = 0
(2) Mechanisms :
Linear combination
Minimal: none of them can be omitted.
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1: C + O = CO X1 = [1,1,-1,0]
2: C + 2 O = CO2 X2 = [1,2,0,-1]
( 3: O + CO = CO2 X3 = [0,1,1,-1] )
4: C + CO2= 2CO X4 = [1,0,-2,1]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2*X1 - X2 = X4
2*X1 - X2 - X4 = 0
(2) Mechanisms :
Linear combination
Minimal: none of them can be omitted.
RC,O,CO,CO2
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1: C + O = CO X1 = [1,1,-1,0]T
2: C + 2 O = CO2 X2 = [1,2,0,-1]T
( 3: O + CO = CO2 X3 = [0,1,1,-1]T)
4: C + CO2= 2CO X4 = [1,0,-2,1]T
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
in general: Y = α1X1+α2X2+...αnXn (M)
α1X1+α2X2+...αnXn – Y = 0
(2) Mechanisms :
Y:= R(M) = the final reaction, determined by the mechanism (M)
+ given start materials and final products . . .
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1: C + O = CO X1 = [1,1,-1,0]T
2: C + 2 O = CO2 X2 = [1,2,0,-1]T
( 3: O + CO = CO2 X3 = [0,1,1,-1]T)
4: C + CO2= 2CO X4 = [1,0,-2,1]T
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
in general: Y = α1X1+α2X2+...αnXn (M)
α1X1+α2X2+...αnXn – Y = 0
(2) Mechanisms :
Y:= R(M) = the final reaction, determined by the mechanism (M)
+ given start materials and final products . . .
Minimal: none of them can be omitted.
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(3) Physical quantities (measure units/”dimension analysis”):
[ 1, 0, 0, 0, 0, 0 ]
[ 0, 1,-1, 0, 0, 0 ]
[-3, 0, 0, 1, 0, 0 ]
[-1, 0,-1, 1, 0, 0 ]
[ 0, 0,-2, 0, 1,-1 ]
[ 0, 0,-3, 1, 0,-1 ]
[ 1, 0,-3, 1, 0,-1 ]
Minimal connection: υ·κ = μ ·c /for some cR/
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(3) Physical quantities (measure units/”dimension analysis”):
[ 1, 0, 0, 0, 0, 0 ]T
[ 0, 1,-1, 0, 0, 0 ]T
[-3, 0, 0, 1, 0, 0 ]T
[-1, 0,-1, 1, 0, 0 ]T
[ 0, 0,-2, 0, 1,-1 ]T
[ 0, 0,-3, 1, 0,-1 ]T
[ 1, 0,-3, 1, 0,-1 ]T
Minimal connection: υ·κ = μ ·c /for some cR/
<=> linear combination of the exponents
exponents
Rexponents
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(4) In General : Main Definition:
S = {s1 , s2 ,… , sk } Rn is an (linear) algebraic simplex
iff S is minimal dependent.
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(4) In General : Main Definition:
S = {s1 , s2 ,… , sk } Rn is an (linear) algebraic simplex
iff S is minimal dependent.
i.e.
S is dependent and S\{si} is independent for all i < k .
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(4) In General : Main Definition:
S = {s1 , s2 ,… , sk } Rn is an (linear) algebraic simplex
iff S is minimal dependent.
i.e.
S is dependent and S\{si} is independent for all i < k .
i.e.
1·s1 + 2·s2 +… + k·sk = 0
and none of them can be omitted : i ≠ 0 for all i < k .
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(4) In General : Main Definition:
S = {s1 , s2 ,… , sk } Rn is an (linear) algebraic simplex
iff S is minimal dependent.
i.e.
S is dependent and S\{si} is independent for all i < k .
i.e.
1·s1 + 2·s2 +… + k·sk = 0
and none of them can be omitted : i ≠ 0 for all i < k .
(minimal reactions, mechanisms, etc. )
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II.
System of
equations
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(0) Homogeneous linear equations:
A·x = 0
Find the structure of minimal solutions
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Question: Assuming AX = 0 , what information could be
extracted from the linear /in/dependency of the rows and columns
of A and of X and of rank(A) ?
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Question: Assuming AX = 0 , what information could be
extracted from the linear /in/dependency of the rows and columns
of A and of X and of rank(A) ?
Answer:
columns of A are the "contents" of the species,
columns of X are the reactions,
generating/independent columns of X denote generating /
independent reactions.
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Question: Assuming AX = 0 , what information could be
extracted from the linear /in/dependency of the rows and columns
of A and of X and of rank(A) ?
Answer:
columns of A are the "contents" of the species,
columns of X are the reactions,
generating/independent columns of X denote generating /
independent reactions.
rows, rank(A), rank(X) = ?
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Observations (reducing the dimension)
a) If a column of A (a species/reaction) is linearly independent
fom the others, then it can be omitted,
since it plays no role in any reaction/ mechanism.
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Observations (reducing the dimension)
a) If a column of A (a species/reaction) is linearly independent
fom the others, then it can be omitted,
since it plays no role in any reaction/ mechanism.
b) If one column of A is parallel to another column,
then one of these columns can be omitted,
since they denote the same species/reaction in multiple dose.
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Observations (reducing the dimension)
a) If a column of A (a species/reaction) is linearly independent
fom the others, then it can be omitted,
since it plays no role in any reaction/ mechanism.
b) If one column of A is parallel to another column,
then one of these columns can be omitted,
since they denote the same species/reaction in multiple dose.
c) If a column of A (a reaction) contains exactly two nonzero
coordinates, then this column can be omitted,
since in this reaction the two species are equivalent.
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Observations (reducing the dimension)
a) If a column of A (a species/reaction) is linearly independent
fom the others, then it can be omitted,
since it plays no role in any reaction/ mechanism.
b) If one column of A is parallel to another column,
then one of these columns can be omitted,
since they denote the same species/reaction in multiple dose.
c) If a column of A (a reaction) contains exactly two nonzero
coordinates, then this column can be omitted,
since in this reaction the two species are equivalent.
Perhaps they are important in chemistry.
NOT the Gauss elminination method.
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x is minimal if for no y we have supp(y) supp(x)
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supp(x) = {ai1,ai2,...,aik : xij0}
x is minimal if for no y we have supp(y) supp(x)
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III.
Algorithm
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(0) Homogeneous linear equations:
A·x = 0
Find all minimal solutions
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Happel-Sellers-Otarod [HOS,1990] 's algorithm for reaction-
mechanisms uses :
- mainly elementary matrix row-column operations
- eliminating equations.
after reductions:
- determine the bases of the solutions with heuristic methods.
Their method is mainly theoretical, non automatic.
No further details are published.
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Reminder: S={s1 , s2 ,… , sk} Rn is an algebraic simplex iff S is dependent
and S\{si} is independent for all i < k .
i.e. 1·s1 + 2·s2 +… + k·sk = 0 and none of them can be omitted.
(minimal reactions, mechanisms, etc. )
Our TASK 1:
Algorithm for generating all simplexes SH in a given HRn.
(all reactions, mechanisms, etc.)
+ Applications
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Reminder: S={s1 , s2 ,… , sk} Rn is an algebraic simplex iff S is dependent
and S\{si} is independent for all i < k .
i.e. 1·s1 + 2·s2 +… + k·sk = 0 and none of them can be omitted.
(minimal reactions, mechanisms, etc. )
Our TASK 1:
Algorithm for generating all simplexes SH in a given HRn.
(all reactions, mechanisms, etc.)
+ Applications
Result: polynomial algorithm
[1991] Hung. J. Ind.Chem. 289-292.
[2000] J. Math. Chem.1-34.
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J.Tóth, A.Nagy, D.Papp:
Reaction Kinetics: Exercises, Programs and Theorems:
Mathematica for Deterministic and Stochastic Kinetics.
Springer, New York, NY, 2018. ISBN:9781493986415,
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IV.
Examples
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E.g.
=>
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V.
Number of
simplexes
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Reminder: S={s1 , s2 ,… , sk} Rn is an algebraic simplex iff S is dependent
and S\{si} is independent for all i < k .
i.e. 1·s1 + 2·s2 +… + k·sk = 0 and none of them can be omitted.
(minimal reactions, mechanisms, etc. )
Task 2:
Question: For given HRn how many simplexes SH could be
in H if |H|=m is given and H spans Rn ?
(how many reactions, mechanisms, etc. )
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Reminder: S={s1 , s2 ,… , sk} Rn is an algebraic simplex iff S is dependent
and S\{si} is independent for all i < k .
i.e. 1·s1 + 2·s2 +… + k·sk = 0 and none of them can be omitted.
(minimal reactions, mechanisms, etc. )
Task 2:
Question: For given HRn how many simplexes SH could be
in H if |H|=m is given and H spans Rn ?
(how many reactions, mechanisms, etc. )
Notation:
simp(H) := the number of simplexes SH .
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Theorem 1 [1995] (Laflamme-Szalkai)
= O(mn+1)
and simp(H) is maximal iff every n -element subset
of H is independent.
1)(
n
mHsimp
Assuming: |H|=m , H spans Rn
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Theorem 1 [1995] (Laflamme-Szalkai)
= O(mn+1)
and simp(H) is maximal iff every n -element subset
of H is independent.
Notes:
- Sperner’s theorem is not enough: what is the structure of H ?
- Vandermonde determinant: xi = [1,i,...,in-1]T (i=1,...,m)
- species are built from n particles and any n species are
independent (and any n+1 are dependent) .
1)(
n
mHsimp
Assuming: |H|=m , H spans Rn
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)(2
/Hsimp
nmn
|H|=m , H spans Rn
Theorem 2 [1995] (Laflamme-Szalkai)
O(m2) =
and simp(H) is minimal iff m/n elements
of H are parallel to bi where {b1,…,bn} is any base of .
(parallel = isomers, multiple doses,…)
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)(2
/Hsimp
nmn
|H|=m , H spans Rn
Theorem 2 [1995] (Laflamme-Szalkai)
O(m2) =
and simp(H) is minimal iff m/n elements
of H are parallel to bi where {b1,…,bn} is any base of .
Proof: similar packing vectors to parallel sets to a base
to reduce simp(H) .
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)(2
/Hsimp
nmn
|H|=m , H spans Rn
Theorem 2 [1995] (Laflamme-Szalkai)
O(m2) =
and simp(H) is minimal iff m/n elements
of H are parallel to bi where {b1,…,bn} is any base of .
More precisely:
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)(2
/Hsimp
nmn
|H|=m , H spans Rn
Theorem 2 [1995] (Laflamme-Szalkai)
O(m2) =
and simp(H) is minimal iff m/n elements
of H are parallel to bi where {b1,…,bn} is any base of .
Open Question:
if no parallel elements are in H ?
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? 1) If n is even => H contains n linearly independent vectors
{ui : i = 1,…,n} and the remaining of H is divided as evenly as
possible between the planes [ui , ui+1] for i = 1, 3, …, n - 1 .
? 2) If n is odd => H again contains n linearly independent vectors
{ui : i = 1,…,n}, one extra vector in the plane [un-1 ,un] and finally the
remaining vectors are divided as evenly as possible between the
planes [ui , ui+1] for i = 1, 3, …, n - 2 with lower indices having
precedence.
LATER !
General Conjecture (1998) (Laflamme, Meng, Szalkai)
no parallel => the minimal configurations in Rn are:
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Reducing the dimension (n=3):
vectors => points, 2D-planes => lines
R3 R2
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So, after the reduction we get:
Definition: (affine) simplexes in R2 are
i) 3 colinear points,
ii) 4 general points: no three colinear,
Elementary question in R2 :
What is the minimal number of (total) simplexes
if the number of points (spanning R2) is m ?
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Theorem 3 [1998] (Laflamme-Szalkai)
For H R3
and for m>8 : simp(H) is minimal iff
|H|=m , H spans Rn , no parallel elements
H =
( vectors = points, planes = lines )
m-2
3
n=3
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Theorem 3 [1998] (Laflamme-Szalkai)
Proof: packing points to lines to reduce simp(H),
many subcases, 14 pp long.
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Reducing the dimension (n=4):
vectors => points, 2D-planes => lines, h.-planes => 2D-planes
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So, after the reduction we get:
Definition: (affine) simplexes in R3 are
i) 3 colinear points,
ii) 4 coplanar, no three colinear,
iii) 5 general points: no three or four as above.
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So, after the reduction we get:
Definition: (affine) simplexes in R3 are
i) 3 colinear points,
ii) 4 coplanar, no three colinear,
iii) 5 general points: no three or four as above.
Still elementary question in R3 :
What is the minimal number of (total) simplexes
if the number of points (spanning R3) is m ?
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Theorem 4 [2010] (Balázs Szalkai - I.Szalkai)
For H R4
and for m>24 simp(H) is minimal iff H is
placed into two (skew) detour line
|H|=m , H spans Rn , no parallel elements
n=4
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Theorem 4 [2010] (Laflamme-Szalkai)
Proof: packing points to planes to reduce simp(H),
using the infinite sides of a tetrahedron
many subcases, 10 pp long.
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? 1) If n is even => H contains n linearly independent vectors
{ui : i = 1,…,n} and the remaining of H is divided as evenly as
possible between the planes [ui , ui+1] for i = 1, 3, …, n - 1 .
General Conjecture (1998) (Laflamme, Meng, Szalkai)
no parallel => the only minimal configurations in Rn are:
[u1, u2] [u3, u4] . . . [ui, ui+1] . . . [un-1, un]
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? 2) If n is odd => H contains n linearly independent vectors
{ui : i = 1,…,n} , one extra vector in the plane [un-1 ,un] and finally
the remaining vectors are divided as evenly as possible between the
planes [ui , ui+1] for i = 1, 3, …, n - 2 with lower indices having
precedence.
General Conjecture (1998) (Laflamme, Meng, Szalkai)
no parallel => the only minimal configurations in Rn are:
[u1, u2] [u3, u4] . . . [ui, ui+1] . . . [un-2, un-1] , [un-1, un]
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VI.
Matroids
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Matroids (hypergraphs) :
What is the minimal and maximal number of cycles and bases
in a matroid of size m and given rank n ?
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[2006] (Laflamme, Dósa, Szalkai) :
Matroids (hypergraphs) :
What is the minimal and maximal number of cycles and bases
in a matroid of size m and given rank n ?
Theorem 5 If m > n+1 then only the uniform matroid Um,n contains
the maximum number of circuits:
If m = n+1 then all matroids of size m and of rank n contain exactly 1
circuit.
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[2006] (Laflamme, Dósa, Szalkai) :
Matroids (hypergraphs) :
What is the minimal and maximal number of cycles and bases
in a matroid of size m and given rank n ?
Theorem 5 If m > n+1 then only the uniform matroid Um,n contains
the maximum number of circuits:
If m = n+1 then all matroids of size m and of rank n contain exactly 1
circuit.
Theorem 6 If m > n then only the uniform matroid Um,n contains
the maximum number of bases:
m
n
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[2006] (Laflamme, Dósa, Szalkai) :
Matroids (hypergraphs) :
What is the minimal and maximal number of cycles and bases
in a matroid of size m and given rank n ?
Theorem 7 For each m and n there is a unique matroid Mo of size m
and of rank n containing the minimum number of bases, namely 1 when
we allow loops in the matroid.
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97
[2006] (Laflamme, Dósa, Szalkai) :
Matroids (hypergraphs) :
What is the minimal and maximal number of cycles and bases
in a matroid of size m and given rank n ?
Theorem 7 For each m and n there is a unique matroid Mo of size m
and of rank n containing the minimum number of bases, namely 1 when
we allow loops in the matroid.
Theorem 8 Any matroid M of size m and of rank n contains the
minimum number m-n circuits if and only if the circuits of the
matroid are pairwise disjoint.
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THM: For each m and n each matroid M contains the minimum
number of bases iff it has a base {a₁,a₂,…,an} such that all other
elements in M are parallel to a₁ .
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THM: For each m and n each matroid M contains the minimum
number of bases iff it has a base {a₁,a₂,…,an} such that all other
elements in M are parallel to a₁ .
PROBLEM Characterize the matroids with the minimum
number of circuits and bases, when neither parallel elements nor
loops are allowed.
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THM: For each m and n each matroid M contains the minimum
number of bases iff it has a base {a₁,a₂,…,an} such that all other
elements in M are parallel to a₁ .
PROBLEM Characterize the matroids with the minimum
number of circuits and bases, when neither parallel elements nor
loops are allowed.
Conjecture [Oxley, 1997] For matroids with k girth(M)
the uniform matroid Um-3,k has minimal number of circuits,
namely
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THM. [2015] (Alahmadi,Aldred,Cruz,Ok,Solé,Thomassen) :
Any loopless matroid M of size μ and rank ν without parallel
elements has at least μ cocircuits .
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VII.
Codes,
Families, ...
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DEF: For n,kN and CC[n,k] linear code (length n dimension k)
M(C) := number of minimal codewords in C
and M(n,k) := max { M(C) | CC[n,k] } .
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DEF: For n,kN and CC[n,k] linear code (length n dimension k)
M(C) := number of minimal codewords in C
and M(n,k) := max { M(C) | CC[n,k] } .
THM. [2013] (Alahmadi,Aldred,Cruz,Solé,Thomassen) : (1nk)
circles of matroids => M(n,k)
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DEF: For n,kN and CC[n,k] linear code (length n dimension k)
M(C) := number of minimal codewords in C
and M(n,k) := max { M(C) | CC[n,k] } .
THM. [2013] (Alahmadi,Aldred,Cruz,Solé,Thomassen) : (1nk)
circles of matroids => M(n,k)
THM. [2015] (Alahmadi,Aldred,Cruz,Ok,Solé,Thomassen) :
circles of matroids => k M(n,k)
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DEF: For n,kN and CC[n,k] linear code (length n dimension k)
M(C) := number of minimal codewords in C
and M(n,k) := max { M(C) | CC[n,k] } .
THM. [2013] (Alahmadi,Aldred,Cruz,Solé,Thomassen) : (1nk)
circles of matroids => M(n,k)
THM. [2015] (Alahmadi,Aldred,Cruz,Ok,Solé,Thomassen) :
circles of matroids => k M(n,k)
THM. [2015] (Alahmadi,Aldred,Cruz,Ok,Solé,Thomassen) :
C has distances 2, circles of matroids =>
M(n,k) / n = a (n-k)+b /
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Corollary [2015] (Alahmadi,Aldred,Cruz,Ok,Solé,Thomassen,
Kashyap) :
For any [n,k] code C of dual distance at least 3 : M(C) n
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G is a connected graph (allowing multiple edges but no loops),
p vertices, q edges.
QUESTION [1981] (Entringer and Slater):
How many cycles #CG a graph with p vertices and q edges can have?
Trivial: #CG < 2q−p+1
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G is a connected graph (allowing multiple edges but no loops),
p vertices, q edges.
QUESTION [1981] (Entringer and Slater):
How many cycles #CG a graph with p vertices and q edges can have?
Trivial: #CG < 2q−p+1
Cycle code C(G) has length n=q, dimension k = q−p+1.
Note: The minimal codewords of C(G) are exactly the incidence
vectors of cycles, that is, circuits in the cycle matroid in G.
THM. [2013] (Aldred,Alahmadi,Cruz,Solé,Thomassen) :
If q>2p+O(log(p)) then #CG < 2q−p .
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THM. [2015] (Alahmadi,Aldred,Cruz,Ok,Solé,Thomassen) :
matroids => In any 2-edge-connected graph with p vertices and
q edges the number of cycles is (the bound is tight) /q=a(p-1)+b/
#CG
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THM. [2015] (Alahmadi,Aldred,Cruz,Ok,Solé,Thomassen) :
matroids => In any 2-edge-connected graph with p vertices and
q edges the number of cycles is (the bound is tight) /q=a(p-1)+b/
#CG
THM. [2015] (Alahmadi,Aldred,Cruz,Ok,Solé,Thomassen) :
matroids =>
Any 3-edge-connected graph with q edges contains at least q cycles ,
the bound is sharp: q #CG
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THM. [2015] (Alahmadi,Aldred,Cruz,Ok,Solé,Thomassen) :
matroids => In any 2-edge-connected graph with p vertices and
q edges the number of cycles is (the bound is tight) /q=a(p-1)+b/
#CG
THM. [2015] (Alahmadi,Aldred,Cruz,Ok,Solé,Thomassen) :
matroids =>
Any 3-edge-connected graph with q edges contains at least q cycles ,
the bound is sharp: q #CG
THM. [2015] (Alahmadi,Aldred,Cruz,Ok,Solé,Thomassen) :
Any 2-connected graph with q edges and p vertices contains at least
#CG
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VIII.
Hypergraphs
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1'st version
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1'st version
(Zs.Tuza, I.Szalkai, 2013)
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Definition: (affine) simplexes in R3 are
i) 3 colinear points,
ii) 4 coplanar, no three colinear,
iii) 5 general points: no three or four as above.
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Definition: (affine) simplexes in R3 are
i) 3 colinear points,
ii) 4 coplanar, no three colinear,
iii) 5 general points: no three or four as above.
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Theorem 3 [1998] (Laflamme-Szalkai) For H R3
m-2 3
Theorem 4 [2010] (Balázs Szalkai - I.Szalkai) For H R4
Mostly contain (affine) simplexes of three points.
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? 1) If n is even => H contains n linearly independent vectors
{ui : i = 1,…,n} and the remaining of H is divided as evenly as
possible between the planes [ui , ui+1] for i = 1, 3, …, n - 1 .
General Conjecture (1998) (Laflamme, Meng, Szalkai)
no parallel => the only minimal configurations in Rn are:
[u1, u2] [u3, u4] . . . [ui, ui+1] . . . [un-1, un]
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? 2) If n is odd => H contains n linearly independent vectors
{ui : i = 1,…,n} , one extra vector in the plane [un-1 ,un] and finally
the remaining vectors are divided as evenly as possible between the
planes [ui , ui+1] for i = 1, 3, …, n - 2 with lower indices having
precedence.
General Conjecture (1998) (Laflamme, Meng, Szalkai)
no parallel => the only minimal configurations in Rn are:
[u1, u2] [u3, u4] . . . [ui, ui+1] . . . [un-2, un-1] , [un-1, un]
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New: no three points are collinear (or: k|S| )
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New: no three points are collinear (or: k|S| )
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New: no three points are collinear (or: k|S| )
k = d := D-1
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Zs.Tuza, I.Szalkai (2014)
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Zs.Tuza, I.Szalkai (2014)
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(asymptotically tight)
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Zs.Tuza, I.Szalkai (2014)
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Yamamoto [1954], Bollobás [1965], Lubell [1966],
Meshalkin [1963]
Hungarian architect Ybl Miklós (1814-1891)
https://en.wikipedia.org/wiki/Mikl%C3%B3s_Ybl
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2'nd version
we let
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2'nd version
Zs.Tuza (2014)
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2'nd version
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2'nd version
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IX.
General
Hierarchy
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X.
Valuation
Operator
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XI.
Graphs
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Petri-graph (P-graph), Volpert-graph,
Feinberg–Horn–Jackson-graph
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Dealing with the chemical structure (an idea) :
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cut
glue
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Many thanks to
You