Necessary and Sufficient Conditions for Oscillations of Neutral Delay Differe...inventionjournals
In this paper, we discuss the oscillatory behavior of all solutions of the first order neutral delay difference equations with several positive and negative coefficients ( ) ( ) ( ) ( ) 0 , i i j j k k I J K x n p x n r x n q x n , o n n (*) where I, J and K are initial segments of natural numbers, pi , rj , qk are positive numbers, i , j are positive integers and k is a nonnegative integer for iI, jJ and kK. We establish a necessary and sufficient conditions for the oscillation of all solutions of (*) is that its characteristic equation ( 1) 1 0 j i k i j k I J K p r q has no positive roots . AMS Subject Classifications : 39A10, 39A12.
Necessary and Sufficient Conditions for Oscillations of Neutral Delay Differe...inventionjournals
In this paper, we discuss the oscillatory behavior of all solutions of the first order neutral delay difference equations with several positive and negative coefficients ( ) ( ) ( ) ( ) 0 , i i j j k k I J K x n p x n r x n q x n , o n n (*) where I, J and K are initial segments of natural numbers, pi , rj , qk are positive numbers, i , j are positive integers and k is a nonnegative integer for iI, jJ and kK. We establish a necessary and sufficient conditions for the oscillation of all solutions of (*) is that its characteristic equation ( 1) 1 0 j i k i j k I J K p r q has no positive roots . AMS Subject Classifications : 39A10, 39A12.
Dealing with Notations and conventions in tensor analysis-Einstein's summation convention covariant and contravariant and mixed tensors-algebraic operations in tensor symmetric and skew symmetric tensors-tensor calculus-Christoffel symbols-kinematics in Riemann space-Riemann-Christoffel tensor.
Exact Matrix Completion via Convex Optimization Slide (PPT)Joonyoung Yi
Slide of the paper "Exact Matrix Completion via Convex Optimization" of Emmanuel J. Candès and Benjamin Recht. We presented this slide in KAIST CS592 Class, April 2018.
- Code: https://github.com/JoonyoungYi/MCCO-numpy
- Abstract of the paper: We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys
𝑚≥𝐶𝑛1.2𝑟log𝑛
for some positive numerical constant C, then with very high probability, most n×n matrices of rank r can be perfectly recovered by solving a simple convex optimization program. This program finds the matrix with minimum nuclear norm that fits the data. The condition above assumes that the rank is not too large. However, if one replaces the 1.2 exponent with 1.25, then the result holds for all values of the rank. Similar results hold for arbitrary rectangular matrices as well. Our results are connected with the recent literature on compressed sensing, and show that objects other than signals and images can be perfectly reconstructed from very limited information.
Dealing with Notations and conventions in tensor analysis-Einstein's summation convention covariant and contravariant and mixed tensors-algebraic operations in tensor symmetric and skew symmetric tensors-tensor calculus-Christoffel symbols-kinematics in Riemann space-Riemann-Christoffel tensor.
Exact Matrix Completion via Convex Optimization Slide (PPT)Joonyoung Yi
Slide of the paper "Exact Matrix Completion via Convex Optimization" of Emmanuel J. Candès and Benjamin Recht. We presented this slide in KAIST CS592 Class, April 2018.
- Code: https://github.com/JoonyoungYi/MCCO-numpy
- Abstract of the paper: We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen? We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys
𝑚≥𝐶𝑛1.2𝑟log𝑛
for some positive numerical constant C, then with very high probability, most n×n matrices of rank r can be perfectly recovered by solving a simple convex optimization program. This program finds the matrix with minimum nuclear norm that fits the data. The condition above assumes that the rank is not too large. However, if one replaces the 1.2 exponent with 1.25, then the result holds for all values of the rank. Similar results hold for arbitrary rectangular matrices as well. Our results are connected with the recent literature on compressed sensing, and show that objects other than signals and images can be perfectly reconstructed from very limited information.
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I am Martin J. I am a Stochastic Processes Assignment Expert at statisticshomeworkhelper.com. I hold a Ph.D. in Stochastic Processes, from Minnesota, USA. I have been helping students with their homework for the past 7 years. I solve assignments related to Stochastic Processes. Visit statisticshomeworkhelper.com or email info@statisticshomeworkhelper.com. You can also call on +1 678 648 4277 for any assistance with Stochastic Processes Assignments.
A Proof of Hypothesis Riemann and it is proven that apply only for equation ζ(z)=0.Also here it turns out that it does not apply in General Case.(DOI:10.13140/RG.2.2.10888.34563/5)
Solutions Manual for An Introduction To Abstract Algebra With Notes To The Fu...Aladdinew
Full download : https://goo.gl/XTCXti Solutions Manual for An Introduction To Abstract Algebra With Notes To The Future Teacher 1st Edition by Nicodemi
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LAB #2Escape VelocityGoal Determine the initial veloc.docxDIPESH30
LAB #2
Escape Velocity
Goal: Determine the initial velocity an object is shot upward from the surface of the
earth so as to never return; illustrate scaling variables to simplify di↵erential equations.
Required tools: dfield and pplane ; second order di↵erential equations; convert-
ing a second order di↵erential equation with missing independent variable to a first
order di↵erential equation; converting a second order di↵erential equation to a system
of di↵erential equations.
Discussion
Newton’s Law of Gravitational Attraction tells us that in general an object of mass
m which is x miles above the surface of the earth will feel a force of approximately
F = �
0.0061m
⇣
1 +
x
4000
⌘2
pounds with the number “4000” an approximation to the radius of the earth in miles
and “0.0061” the acceleration due to gravity measured in miles/sec2.
On the other hand, according to Newton’s 2nd Law
F = m
d
2
x
dt
2
.
Thus equating these expressions for F leads to the di↵erential equation
d
2
x
dt
2
= �
0.0061
⇣
1 +
x
4000
⌘2 .
To study this equation using the Student Edition of Matlab, we need to change the
units to avoid overly large intervals. Rather than measuring distance in miles, we will
use multiples of the radius of the earth. This amounts to making the substitution
y =
x
4000
(scaling the dependent variable) resulting in the equation
d
2
y
dt
2
= �
0.0061
4000
1
(1 + y)2
. (A)
We can further simplify this equation by changing the units of time (scaling the
independent variable). If we let
s =
r
0.0061
4000
t
1
then by the Chain Rule
dy
dt
=
dy
ds
ds
dt
=
r
0.0061
4000
dy
ds
and hence
d
2
y
dt
2
=
d
dt
✓
dy
dt
◆
=
d
dt
r
0.0061
4000
dy
ds
!
d
2
y
dt
2
=
r
0.0061
4000
d
dt
✓
dy
ds
◆
d
2
y
dt
2
=
r
0.0061
4000
d
ds
✓
dy
ds
◆
ds
dt
�
d
2
y
dt
2
=
0.0061
4000
d
2
y
ds
2
Using the last expression, the di↵erential equation (A) becomes
d
2
y
ds
2
= �
1
(1 + y)2
. (B)
Hence y is now viewed as a function of s, instead of t.
Equation (B) is a second order di↵erential equation whose independent variable s
is missing. We can convert this special type of 2nd order di↵erential equation to a first
order equation using a simple technique. Let
v =
dy
ds
.
This is simply the velocity of the projectile using s as the unit of time instead of t and
y as the distance instead of x.
Since
dy
ds
= v, from the Chain Rule we obtain
d
2
y
ds
2
=
dv
ds
=
dv
dy
dy
ds
= v
dv
dy
.
Thus, equation (B) can be written as
v
dv
dy
= �
1
(1 + y)2
. (C)
Equation (C) is a 1st order di↵erential equation which may be solved to express v
as a function of y.
Assignment
(1) Enter equation (C) into dfield and plot a few solutions starting at y = 0 and
various initial values for the velocity v using the dfield default ranges for v and y.
You will need to enter it in the form
dv
dy
= �
1
v (1 + y)2
. (D)
(If you have trouble with dfield crashing, see the ...
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1. MOP 2007 Black Group Algebraic Techniques in Combinatorics Yufei Zhao
Algebraic Techniques in Combinatorics
June 26, 2007
Yufei Zhao
yufeiz@mit.edu
Linear algebra
Useful facts in linear algebra
Any set of n+1 vectors in an n-dimensional vector space is linearly dependent. That is, we can
2. nd scalars a1; : : : ; an+1, not all zero, such that a1v1 + + an+1vn+1 = 0.
Almost all linear algebra results (especially the ones related to rank) are true over any
4. eld F2 is often useful for working with parity or incidence. However, note that we
might not be able to use eigenvalues.
Suppose that A is an m n matrix, then the subspace spanned by its columns has the same
dimension as the subspace spanned by its rows. This common dimension number is called the
rank of A. In particular, we have rankA min(m; n).
Rank-nullity theorem: rankA + nullityA = n.
rank(AB) min(rank A; rankB)
rank(A + B) rankA + rankB
A is invertible if and only if m = n = rankA. If A is a square matrix, then it is invertible if and
only if there does not exist a nonzero vector v such that Av = 0. Or equivalently, none of the
eigenvalues of A is zero.
The determinant of a square matrix A can be evaluated by taking a sum over all permutations
of f1; 2; : : : ; ng
detA =
X
2Sn
(1)sgn a1(1)a2(2) an(n):
A matrix is invertible if an only if its determinant is nonzero. In F2, we no longer have to worry
about the sign in front of the product.
If W is a subspace of V , denote W? = fv 2 V j vtw = 0 8w 2 Wg, then
dimW + dimW? = dim V:
However, it's not necessarily true that W W? = V ! E.g., take W = f(0; 0); (1; 1)g in F22
, then
W? = W.
Suppose that we have a set of maps fi : V ! R, and points vi 2 V , 1 i n. If fi(vi)6= 0 and
fi(vj) = 0 whenever i6= j, then f1; f2; : : : ; fn are linearly independent.
1
5. MOP 2007 Black Group Algebraic Techniques in Combinatorics Yufei Zhao
Problem: (St. Petersburg) Students in a school go for ice cream in groups of at least two. After
k 1 groups have gone, every two students have gone together exactly once. Prove that the number
of students in the school is at most k.
Solution. There is a combinatorial solution which is somewhat long and non-intuitive. However, a
much quicker (and more intuitive) solution is available using the tools of linear algebra.
Let there be n students. Note if some student went for ice cream only once, then everyone else has
to have gone with that student, due to the constraint that every pair of student have gone together
exactly once. Furthermore, since each group consists of two students, no other groups can be formed.
However, k 1, so this situation cannot occur. Therefore, every student went for ice cream at least
twice.
Let us construct incidence vectors vi 2 Rk; 1 1 n, representing the students. That is, the
j-th component of vi is 1 if student i went with the j-th group, and 0 otherwise.
The condition that every two students have gone together exactly once translates into the dot
product vi vj = 1 for i6= j. The condition that every student went for ice cream at least twice
translates into jvij2 2.
We want to prove that n k. Suppose otherwise. Then fv1; : : : ; vng consists of at least k + 1
vectors in Rk, so they must be linearly dependent. It follows that there are real numbers 1; : : : ; n,
not all zero, such that
1v1 + + nvn = 0:
Let us square the above expression (i.e., taking the dot product with itself. We get
0 =
Xn
i=1
2
i jvij2 + 2
X
ij
ijvi vj =
Xn
i=1
2
i jvij2 + 2
X
ij
ij =
Xn
i=1
2
i
jvij2 1
+
Xn
i=1
i
!2
:
However, the RHS expression is positive, since jvij2 2 and some i is nonzero. Contradiction.
Alternatively, we can use an incidence matrix to obtain a somewhat faster (albeit more tech-
nically involved) solution. The incidence matrix M is a k n matrix, where Mij is 1 if the i-th group
includes student j. Using the notation in the previous solution, we can write this as
M =
0
@
j j j
v1 v2 vn
j j j
1
A
Consider the product MtM. Since vi vj = 1 for i6= j and jvij2 2 for all i, we see that MtM = J+A,
where J is the nn matrix with all entries 1, and A is a diagonal matrix with positive diagonal entries.
Note that J is positive semide
7. nite
(meaning that xtJx 0 for all x6= 0). Therefore, their sum J + A = MtM is positive de
8. nite. In
particular, this means that MtM is invertible, so its rank is n. Since M is a kn matrix, we conclude
that k n.
2
9. MOP 2007 Black Group Algebraic Techniques in Combinatorics Yufei Zhao
Partially ordered sets (posets)
De
10. nition. A partially ordered set (or poset for short) P is a set, also denoted P, together with a
binary relation denoted satisfying the following axioms:
(re
exivity) x x for all x 2 P
(antisymmetry) If x y and y x, then x = y.
(transitivity) If x y and y z, then x z.
An example of a poset is the set of all subsets of f1; 2; : : : ; ng under the relation . This poset is
sometimes called the Boolean algebra of rank n, and denoted Bn.
The Hasse diagram is a simple way of representing (small) posets. We say that x covers y if
x y (i.e., x y and x6= y) and there is no z 2 P such that x z y. If x covers y, then we
draw x above y and connect them using a line segment. Note that in general, x y if and only if x
is above y and we trace a downward path from x to y in the Hasse diagram. The Hasse diagram for
B3 is depicted below.
f1; 2; 3g
tt tt tt tt t
f1; 2g
JJJJJJJJJ
f1; 3g f2; 3g
f1g
ttt ttt ttt t
f2g
JJJJJJJJJJ
ttt ttt ttt t
f3g
JJJJJJJJJJ
;
JJJJJJJJJJJ
tt tt tt tt tt t
There is one result about posets that has proven useful for olympiad problems. Before we state
this result, let us go over some more terminology.
Two elements x; y of a poset are called comparable if x y or x y, otherwise they are called
incomparable. A chain is a sequence of elements a1 a2 ak, and an antichain is a set of
pairwise incomparable elements. The length or a chain or antichain is one less the number of elements
contained in it.
Now we are ready to state Dilworth's Theorem.
Theorem 1. (Dilworth) Let P be a
11. nite poset. Then the smallest set of chains whose union is P
has the same cardinality as the longest antichain.
There is also a dual version of this theorem that's much easier to prove.
Theorem 2. Let P be a
12. nite poset. Then the smallest set of antichains whose union is P has the
same cardinality as the longest chain.
The number of elements in the longest chain is height of the poset, and the number of elements
in the longest antichain is width of the poset. Another way of stating the two theorems is that if a
poset P has height h and width w, then P can be covered with h antichains and w chains. One simple
consequence is that jPj h w.
3
13. MOP 2007 Black Group Algebraic Techniques in Combinatorics Yufei Zhao
Instead of proving the the above two theorems, which you can do yourself1, let's see how we can
apply them.
Problem: (Romania TST 2005) Let n be a positive integer and S a set of n2 + 1 positive integers
with the property that every (n+1)-element subset of S contains two numbers one of which is divisible
by the other. Show that S contains n + 1 dierent numbers a1; a2; : : : ; an+1 such that ai j ai+1 for
each i = 1; 2; : : : ; n.
Solution. Use the divisibility relation to obtain a poset on S (that is, x y i x j y. Check that
this makes a poset). The condition that there does not exist an n + 1 element subset of S that no
element divides another translates into the condition that there does not exist an antichain with n+1
elements in S. So the longest antichain in S has at most n elements, and thus by Dilworth's theorem,
S can be written as the union of at most n chains. Since S has n2 + 1 elements, this implies that one
of these chains has a least n + 1 elements. This implies the result.
A very similar result is the Erdos{Szekeres Theorem, which states that within any sequence
of ab+1 real numbers, there is either a nondecreasing subsequence of a+1 terms, or a nonincreasing
subsequence of b + 1 terms. This result is also a simple consequence of Dilworth's Theorem.
More examples are given in the problems section.
Problems related to linear algebra
1. (Nonuniform Fisher inequality) Let A1; : : : ;Am be distinct subsets of f1; 2; : : : ; ng. Suppose that
there is an integer 1 n such that jAi Aj j = for all i6= j. Prove that m n.
2. (a) (China West 2002) Let A1;A2; : : : ;An+1 be non-empty subsets of f1; 2; : : : ; ng. Prove that
there exists nonempty disjoint subsets I; J f1; 2; : : : ; n + 1g such that
[
k2I
Ak =
[
k2J
Ak:
(b) (Lindstrom) Let A1;A2; : : : ;An+2 be non-empty subsets of f1; 2; : : : ; ng. Prove that there
exists nonempty disjoint subsets I; J f1; 2; : : : ; n + 2g such that
[
k2I
Ak =
[
k2J
Ak; and
k2I
Ak =
k2J
Ak:
3. (Russia 2001) A contest with n question was taken by m contestants. Each question was worth a
certain (positive) number of points, and no partial credits were given. After all the papers have
been graded, it was noticed that by reassigning the scores of the questions, any desired ranking
of the contestants could be achieved. What is the largest possible value of m?
4. Oddtown and Eventown. In a certain town with n citizens, a number of clubs are set up. No
two clubs have exactly the set of members. Determine the maximum number of clubs that can
be formed under each of the following constraints:
(a) The size of every club is odd, and every pair of clubs share an even number of members.
1if you are really stuck, then you may consult the proof at, for example, http://ocw.mit.edu/NR/rdonlyres/
Mathematics/18-997Spring2004/FC143A49-2C1F-4653-85C4-C08A74990438/0/co_lec6.pdf
4
14. MOP 2007 Black Group Algebraic Techniques in Combinatorics Yufei Zhao
(b) The size of every club is even, and every pair of clubs share an odd number of members.
(c) The size of every club is even, and every pair of clubs share an even number of members.
(d) The size of every club is odd and every pair of clubs share an odd number of members.
5. (Moldova TST 2005) Does there exist a con
15. guration of 22 distinct circles and 22 distinct points
on the plane, such that every circle contains at least 7 points and every point belongs at least
to 7 circles?
6. (Iran TST 1996, Germany TST 2004) Let G be a
16. nite simple graph, and there is a light bulb
at each vertex of G. Initially, all the lights are o. Each step we are allowed to chose a vertex
and toggle the light at that vertex as well as all its neighbors'. Show that we can get all the
lights to be on at the same time.
7. Let a1; a2; : : : ; an be integers. Show that
Y
1ijn
ai aj
i j
is an integer. (Hint: use the Vandermonde determinant.)
8. (a) Let G be a graph with v vertices. Let f(n) denote the number of closed walks in G of
length n. Show that there exists complex numbers 1; : : : ; v such that
f(n) = n1
+ n2
+ + nv
for all positive integers n.
(b) Let g(n;m) denote the number of sequences (x1; x2; ; xn), with terms from f1; 2; ;mg,
such that x1 = 1, xn6= 1, and xi6= xi+1 for any i. Show that
g(n;m) =
1
m
((m 1)n + (m 1)(1)n) :
9. (Crux 3037) There are 2007 senators in a senate. Each senator has enemies within the senate.
Prove that there is a non-empty subset K of senators such that for every senator in the senate,
the number of enemies of that senator in the set K is an even number.
10. (Classical) Let a1; a2; : : : ; a2n+1 be real numbers, such that for any 1 i 2n + 1, we can
remove ai and separate the remaining 2n numbers into two groups with equal sums. Show that
a1 = a2 = = a2n+1.
11. (Russia 1998) Each square of a (2n 1) (2n 1) board contains either +1 or 1. Such an
arrangement is called successful if each number is the product of its neighbors (squares sharing
a common side with the given square). Find the number of successful arrangements.
12. (Graham{Pollak) Show that the complete graph with n vertices, Kn, cannot be covered by fewer
than n 1 complete bipartite graphs so that each edge of Kn is covered exactly once.
13. (Iran 2006) Let B be a set of n-tuples of integers such that for every two distinct members
(a1; : : : ; an) and (b1; : : : ; bn) of B, there exist 1 i n such that ai bi + 1 (mod 3). Prove
that jBj 2n.
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17. MOP 2007 Black Group Algebraic Techniques in Combinatorics Yufei Zhao
14. (a) (Frankl{Wilson) Let A1;A2; : : : ;Am be distinct subset of f1; 2; : : : ; ng. Let L be the set of
numbers that occur as jAi Aj j for some i6= j, and suppose that jLj = s. Show that
m
n
s
+
n
s 1
+ +
n
0
(b) (Ray-Chaudhuri{Wilson) Let 0 k n be positive integers, and let A1;A2; : : : ;Am be
distinct k-element subsets of f1; 2; : : : ;mg. Let L be the set of numbers that occur as
jAi Aj j for some i6= j, and suppose that jLj = s. Show that m
n
s
.
Problems related to posets
1. (Sperner) Let A1; : : : ;Ak be subsets of f1; 2; : : : ; ng so that no Ai contains another Aj . Show
that
Xk
i=1
1n
jAij
1:
Conclude that k
n
bn=2c
.
2. (Romanian TST 2006) Let m and n be positive integers and S be a subset of f1; 2; 3; : : : ; 2mng
with (2m 1)n + 1 elements. Prove that S contains m+ 1 distinct numbers a0; a1; : : : ; am such
that ak1 j ak for all k = 1; 2; : : : ;m.
3. (Iran 2006) Let k be a positive integer, and let S be a
18. nite collection of intervals on the real line.
Suppose that among any k + 1 of these intervals, there are two with a non-empty intersection.
Prove that there exists a set of k points on the real line that intersects with every interval in S.
4. (Slovak competition 2004) Given 1001 rectangles with lengths and widths chosen from the set
f1; 2; : : : ; 1000g, prove that we can chose three of these rectangles, A;B;C, such that A
20. ts into C.
5. Let G be a simple graph, and let (G) be its chromatic number, i.e., the smallest number of
colors needed to color its vertices so that no edge connects two vertices of the same color. Show
that there is a path in G of length (G) such that all (G) vertices are of dierent colors.
6. Suppose that A and B are two distinct lattice points in Rn with non-negative integer coordinates.
We say that A dominates B (denote by A B) if all the components of AB are nonnegative,
and A6= B.
(a) Suppose that S is an in
21. nite sequence of lattice points in Rn with non-negative coordinates.
Show that there exists an in
22. nite subsequence satisfying Si1 Si2 Si3 .
(b) Suppose that T is a set of lattice points in the box [0; t1] [0; t2] [0; tn] (where
t1; t2; : : : ; tn are
23. xed nonnegative integers). It is known that no element in T dominates
another element. What is the maximum value of jTj?
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