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The Solution to P Versus NP and The Riemann Hypothesis.
[[Image:Complexity classes.svg|thumb|250px|Diagram of complexity classes provided that '''P''' ≠ '''NP'''. The existence of problems outside both '''P''' and '''NP'''-complete in this case was established by Ladner.<ref name="Ladner">R. E. Ladner "On the structure of polynomial time reducibility," J.ACM, 22, pp. 151&ndash;171, 1975. Corollary 1.1. [http://portal.acm.org/citation.cfm?id=321877&dl=ACM&coll=&CFID=15151515&CFTOKEN=6184618 ACM site].</ref>]]
The definitive solutions to both the Riemann Hypothesis and P Versus NP.


Wednesday, February 25, 2009
The relationship between the '''[[complexity class]]es [[P (complexity)|P]] and [[NP (complexity)|NP]]''' is an unsolved question in [[theoretical computer science]]. It is considered to be the most important problem in the field – the [[Clay Mathematics Institute]] has offered a $1 million US prize for the first correct proof.<ref name="CMI Millennium Prize Problems">{{cite web|title=Millennium Prize Problems|url=http://www.claymath.org/millennium/|date=2000-05-24|accessdate=2008-01-12}}</ref>
PversusNP
Indeed, Mr. Musatov's work is correct and I do hereby formally acknowledge and verify the correctness of his work and openly acknowledge him as amongst out my peers. The day it will be said it was today I have seen it and will state whole it has been proven yes that good God indeed, P equals NP.--Signed Stephen A. Cook, (Congratulations, Martin)


Stephen A. Cook
In essence, the question '''P''' = '''NP'''? asks: if 'yes'-answers to a [[decision problem|'yes'-or-'no'-question]] can be ''verified'' "quickly" (in [[polynomial time]]), can the answers themselves also be ''computed'' quickly?
Distinguished University Professor
Department of Computer Science
University of Toronto
Toronto, Canada M5S 3G4


Tel: (416) 978-5183
Consider, for instance, the [[Subset sum problem|subset-sum problem]], an example of a problem which is "easy" to verify, but whose answer is ''believed'' (but not proven) to be "difficult" to compute. Given a set of [[integer]]s, does some nonempty [[subset]] of them sum to 0? For instance, does a subset of the set {{nowrap| {−2, −3, 15, 14, 7, −10} }} add up to 0? The answer "yes, because {{nowrap| {−2, −3, −10, 15} }} add up to zero", can be quickly verified with a few additions. However, finding such a subset in the first place could take much longer. The information needed to verify a positive answer is also called a ''certificate''. Given the right certificates, "yes" answers to our problem can be verified in polynomial time, so this problem is in '''NP'''.
sacook [at] cs [dot] toronto [dot] edu


For press inquiries, please contact: James Carlson or David Ellwood, (617) 995 2600, Email: carlson [at] claymath.org; ellwood [at] claymath.org.
An answer to the '''P''' = '''NP''' question would determine whether problems like the subset-sum problem are as "easy" to compute as to verify. If it turned out '''P''' does not equal '''NP''', it would mean that some '''NP''' problems are substantially "harder" to compute than to verify.


The restriction to yes/no problems is unimportant; the resulting problem when more complicated answers are allowed (whether '''[[FP (complexity)|FP]]''' = '''[[FNP (complexity)|FNP]]''') is equivalent.<ref>{{CZoo|Class FP|F#fp}}: "FP = FNP if and only if P = NP". </ref>


NP: Nondeterministic Polynomial-Time
{{Millennium Problems}}
02.24.2009
(→P = ⋯⋮⋱⋮⋯ N ⋯⋮⋱⋮⋯ P)
http://qwiki.stanford.edu/wiki/Complexity_Zoo:N#np
Proof: published
(M.M.M)
If it is known that if any NP-complete language is sparse (contains no more than a polynomial number of strings of length n), then P = NP. [BH08] improved this result, showing that if any language in NP has an NP-hard set of subexponential density, then coNP is contained in NP/poly and thus, by [Yap82], PH collapses to the third level. Conceptually, a decision problem is a problem that takes as input some string, and outputs "yes" or "no". If there is an algorithm (say a Turing machine, or a computer program with unbounded memory) which is able to produce the correct answer for any input string of length Failed to parse (<math_output_error>): n
in at most c \cdot n^k steps, where k and Failed to parse (<math_output_error>): c are constants independent of the input string, then we say that the problem can be solved in polynomial time and we place it in the class P. Formally, P is defined as the set of all languages which can be decided by a deterministic polynomial-time Turing machine. That is,
P = {L:L = L(M) for some deterministic polynomial-time Turing machine M}
where L(M) = \{ w\in\Sigma^{*}: M \text{ accepts } w \}
and a deterministic polynomial-time Turing machine is a deterministic Turing machine M which satisfies the following two conditions: 1. M halts on all input w; and 2. there exists k \in N such that T_{M}(n)\in\; O(nk), where T_{M}(n) = \max\{ t_{M}(w) : w\in\Sigma^{*}, \leftw\right = n \} and tM(w) = number of steps M takes to halt on input w.
NP can be defined similarly using nondeterministic Turing machines (the traditional way). However, a modern approach to define NP is to use the concept of certificate and verifier. Formally, NP is defined as the set of languages over a finite alphabet that have a verifier that runs in polynomial time, where the notion of "verifier" is defined as follows.
Let Failed to parse (<math_output_error>): L
be a language over a finite alphabet, Σ.
L\in\mathbf{NP} if, and only if, there exists a binary relation R\subset\Sigma^{*}\times\Sigma^{*} and a positive integer k such that the following two conditions are satisfied: 1. For all x\in\Sigma^{*}, x\in L \Leftrightarrow\exists y\in\Sigma^{*} such that (x,y)\in R\; and \lefty\right\in\;O(\leftx\right^{k}); and 2. the language L_{R} = \{ x\# y:(x,y)\in R\} over \Sigma\cup\{\#\} is decidable by a Turing machine in polynomial time.
A Turing machine that decides LR is called a verifier for L and a y such that (x,y)\in R is called a certificate of membership of x in L.
In general, a verifier does not have to be polynomial-time. However, for L to be in NP, there must be a verifier that runs in polynomial time. --MartinMichaelMusatov 07:18, 24 February 2009 (UTC)
[edit] NPC: NP-Complete The class of decision problems such that (1) they're in NP and (2) every problem in NP is reducible to them (under some notion of reduction). In other words, the hardest problems in NP. Two notions of reduction from problem A to problem B are usually considered:
Karp or many-one reductions. Here a polynomial-time algorithm is given as input an instance of problem A, and must produce as output an instance of problem B.
Turing reductions, in this context also called Cook reductions. Here the algorithm for problem B can make arbitrarily many calls to an oracle for problem A.Some examples of NP-complete problems are discussed under the entry for NP. The classic reference on NPC is [GJ79]. Unless P = NP, NPC does not contain any sparse problems: that is, problems such that the number of 'yes' instances of size n is upper-bounded by a polynomial in n [Mah82]. A famous conjecture [BH77] asserts that all NP-complete problems are polynomial-time isomorphic -- i.e. between any two problems, there is a one-to-one and onto Karp reduction. If that's true, the NP-complete problems could be interpreted as mere "relabelings" of one another. NP-complete problems are p-superterse unless P = NP [BKS95]. This means that, given k Boolean formulas F1,...,Fk, if you can rule out even one of the 2k possibilities in polynomial time (e.g., "if F1,...,Fk-1 are all unsatisfiable then Fk is satisfiable"), then P = NP.
[BH08] H. Buhrman and J. Hitchcock. NP-Hard sets are exponentially eense unless NP is contained in coNP/poly, Electronic Colloquium on Computational Complexity, ECCC Report TR08-022, accepted on Mar 11, 2008. http://eccc.hpi-web.de/eccc-reports/2008/TR08-022/index.html
[Yap83] C. Yap. Some consequences of non-uniform conditions on uniform classes, Theoretical Computer Science, (1983), 26, 287-300.
[GJ79] M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman, 1979.
[Mah82] S. R. Mahaney. Sparse complete sets for NP: Solution of a conjecture by Berman and Hartmanis, Journal of Computer and System Sciences 25:130-143, 1982.
[BH77] L. Berman and J. Hartmanis. On isomorphism and density of NP and other complete sets, SIAM Journal on Computing 6:305-322, 1977.
[BKS95] R. Beigel, M. Kummer, and F. Stephan. Approximable sets, Information and Computation 120(2):304-314, 1995. http://www.cis.temple.edu/~beigel/papers/bks-queries2-ic.PS.gz.


BinaryChaosTheory.jpg
==Context of the problem==


i
The relation between the '''[[complexity class]]es P and NP''' is studied in [[computational complexity theory]], the part of the [[theory of computation]] dealing with the resources required during computation to solve a given problem. The most common resources are time (how many steps it takes to solve a problem) and space (how much memory it takes to solve a problem).
...... .. ............ .... ................
MARTIN M. MUSATOV
An Open Address to Mr. Stephen A. Cook


(r=0)
In such analysis, a model of the computer for which time must be analyzed is required. Typically, such models assume that the computer is [[Deterministic computation|''deterministic'']] (given the computer's present state and any inputs, there is only one possible action that the computer might take) and ''sequential'' (it performs actions one after the other). As of 2008, these assumptions are satisfied by all practical computers yet devised, even those featuring [[parallel computing]].{{Fact|date=January 2008}}
1. STATEMENT OF THE SOLUTION


In this theory, the class '''[[P (complexity)|P]]''' consists of all those ''[[decision problem]]s'' (defined [[#Formal definitions for P and NP|below]]) that can be solved on a deterministic sequential machine in an amount of time that is [[polynomial]] in the size of the input; the class '''[[NP (complexity)|NP]]''' consists of all those decision problems whose positive solutions can be verified in [[polynomial time]] given the right information, or equivalently, whose solution can be found in polynomial time on a [[Non-deterministic Turing machine|non-deterministic]] machine.<ref>Sipser, Michael: ''Introduction to the Theory of Computation, Second Edition, International Edition'', page 270. Thomson Course Technology, 2006. Definition 7.19 and Theorem 7.20.</ref> Arguably, the biggest open question in [[theoretical computer science]] concerns the relationship between those two classes:
:Is '''P''' equal to '''NP'''?
In a 2002 poll of 100 researchers, 61 believed the answer is no, 9 believed the answer is yes, 22 were unsure, and 8 believed the question may be independent of the currently accepted axioms, and so impossible to prove or disprove.<ref name="poll">{{cite journal|author=William I. Gasarch|title=The P=?NP poll.|journal=SIGACT News|volume=33|issue=2|pages=34–47|month=June | year=2002|url=http://www.cs.umd.edu/~gasarch/papers/poll.pdf|doi=10.1145/1052796.1052804|format=PDF|accessdate=2008-12-29}}</ref>


==Formal definitions for P and NP==


Conceptually, a ''decision problem'' is a problem that takes as input some [[String (computer science)|string]], and outputs "yes" or "no". If there is an [[algorithm]] (say a [[Turing machine]], or a [[Computer programming|computer program]] with unbounded memory) which is able to produce the correct answer for any input string of length <math>n</math> in at most <math>c \cdot n^k</math> steps, where <math>k</math> and <math>c</math> are constants independent of the input string, then we say that the problem can be solved in ''polynomial time'' and we place it in the class '''P'''. Formally, '''P''' is defined as the set of all languages which can be decided by a deterministic polynomial-time Turing machine. That is,


T
'''P''' = <math>\{ L : L=L(M) \text{ for some deterministic polynomial-time Turing machine } M \}</math>


where <math>L(M) = \{ w\in\Sigma^{*}: M \text{ accepts } w \}</math>


and a deterministic polynomial-time Turing machine is a deterministic Turing machine <math>M</math> which satisfies the following two conditions:


his solution to P versus NP explains how every language accepted by some non deterministic algorithm in polynomial
#<math>M \text{ halts on all input } w</math>; and
time can be accepted by some (deterministic) algorithm in polynomial time. The central point in polar
#there exists <math>k \in N</math> such that <math>T_{M}(n)\in\; </math>[[Big O notation#Formal definition|''O'']]<math>(n^{k})</math>,
coordinates, or the point with all zero coordinates (0, ..., 0) in Cartesian coordinates is the binary point of origin [1]: .
::where <math>T_{M}(n) = \max\{ t_{M}(w) : w\in\Sigma^{*}, \left|w\right| = n \}</math>
::and <math>t_{M}(w) = \text{ number of steps M takes to halt on input } w.</math>


'''NP''' can be defined similarly using nondeterministic Turing machines (the traditional way). However, a modern approach to define '''NP''' is to use the concept of ''[[Certificate (complexity)|certificate]]'' and ''verifier''. Formally, '''NP''' is defined as the set of languages over a finite alphabet that have a verifier that runs in polynomial time, where the notion of "verifier" is defined as follows.


In three dimensions, the x-axis, y-axis, and z-axis meet at the origin.
Let <math>L</math> be a language over a finite alphabet, <math>\Sigma</math>.


<math>L\in\mathbf{NP}</math> if, and only if, there exists a binary relation <math>R\subset\Sigma^{*}\times\Sigma^{*}</math> and a positive integer <math>k</math> such that the following two conditions are satisfied:


The matrix form for these coordinates may be expressed:
#For all <math>x\in\Sigma^{*}</math>, <math>x\in L \Leftrightarrow\exists y\in\Sigma^{*}</math> such that <math>(x,y)\in R\;</math> and <math>\left|y\right|\in\;</math>[[Big O notation#Formal definition|''O'']]<math>(\left|x\right|^{k})</math>; and
The derivatives of the unit vectors are then:
#the language <math>L_{R} = \{ x\# y:(x,y)\in R\}</math> over <math>\Sigma\cup\{\#\}</math> is decidable by a Turing machine in polynomial time.


A Turing machine that decides <math>L_{R}</math> is called a ''verifier'' for <math>L</math> and a <math>y</math> such that <math>(x,y)\in R</math> is called a ''certificate of membership'' of <math>x </math> in <math>L</math>.


In general, a verifier does not have to be polynomial-time. However, for <math>L</math> to be in '''NP''', there must be a verifier that runs in polynomial time.


===Example===


Let <math>\mathit{COMPOSITE} = \{x\in N:x=pq \;\text{for integers}\; p, q > 1 \}</math> and <math>R = \{(x,y)\in N\times N: 1<y< \sqrt x\; ; \;y\; \text{divides}\; x\}</math>.


To formally define the solution it is indeed necessary to observe the model of a computer, or Turing machine and process
Clearly, the question of whether a given <math>x</math> is a composite is equivalent to the question of whether <math>x</math> is a member of <math>\mathit{COMPOSITE}</math>. It can be shown that <math>\mathit{COMPOSITE}\in\mathbf{NP}</math> by verifying that <math>\mathit{COMPOSITE}</math> satisfies the above definition.
information in “real-time” as it is received as a computable function or linear stream.
By this declaration, formally, the class P contains the indecision problems
P =
.
...
.
N
.
...
.
P
From this point, we can continue the expansion:
The area of a circle [2]: . ..=....2
The binomial theorem [3]: ..+.. ..= ..
..
........-..
..
..=0
Expansion of a sum (Taylor Series) [4];.
1+.. ..=1+
....
1!
+
.. ..-1 ..22!
+.
Followed by the Fourier Series [5]:.
.. .. =..0+ ....cos
......
..
+....sin
......
..
8
..=1
The Pythagorean Formula [6]:. ..2+..2=..2


<math>\mathit{COMPOSITE}</math> also happens to be in '''P''' <ref name="Agrawal">{{cite web|author=M. Agrawal, N. Kayal, N. Saxena|title=Primes is in P|url=http://www.cse.iitk.ac.in/users/manindra/algebra/primality_v6.pdf|format=PDF|accessdate=2008-12-29}}</ref><ref>[[AKS primality test]]</ref>.


==NP-complete==


To attack the '''P''' = '''NP''' question, the concept of [[NP-complete|'''NP'''-completeness]] is very useful. Informally, the '''NP'''-complete problems are the "toughest" problems in '''NP''' in the sense that they are the ones most likely not to be in '''P'''. '''NP'''-complete problems are those '''NP'''-hard problems which are in '''NP''', where [[NP-hard|'''NP'''-hard]] problems are those to which ''any'' problem in '''NP''' can be reduced in polynomial time. For instance, the decision problem version of the [[traveling salesman problem]] is '''NP'''-complete, so ''any'' instance of ''any'' problem in '''NP''' can be transformed mechanically into an instance of the traveling salesman problem, in polynomial time. The traveling salesman problem is one of many such '''NP'''-complete problems. If any '''NP'''-complete problem is in '''P''', then it would follow that '''P''' = '''NP'''. Unfortunately, many important problems have been shown to be '''NP'''-complete and as of 2009, not a single fast algorithm for any of them is known.


Based on the definition alone, it's not obvious that '''NP'''-complete problems exist. A trivial and contrived '''NP'''-complete problem can be formulated as: given a description of a Turing machine M guaranteed to halt in polynomial time, does there exist a polynomial-size input that M will accept?<ref name="Scott">{{cite web|author=Scott Aaronson|title=PHYS771 Lecture 6: P, NP, and Friends|url=http://www.scottaaronson.com/democritus/lec6.html |accessdate=2007-08-27}}</ref> It is in '''NP''' because, given an input, it is simple to check whether or not M accepts the input by simulating M; it is '''NP'''-hard because the verifier for any particular instance of a problem in '''NP''' can be encoded as a polynomial-time machine M that takes the solution to be verified as input. Then the question of whether the instance is a yes or no instance is determined by whether a valid input exists.


1 Arfken, G. "Special Coordinate Systems--Rectangular Cartesian Coordinates." §2.3 in Mathematical Methods for Physicists, 3rd ed. Orlando, FL: Academic
The first natural problem proven to be '''NP'''-complete was the [[Boolean satisfiability problem]]. This result
Press, pp. 94-95, 1985.
came to be known as [[Cook–Levin theorem]]; its
proof that satisfiability is NP-complete contains technical details about Turing machines as they relate to the definition of '''NP'''. However, after this problem was proved to be NP-complete, [[reduction (complexity)|proof by reduction]] provided a simpler way to show that many other problems are in this class. Thus, a vast class of seemingly unrelated problems are all reducible to one another, and are in a sense the "same problem".


==Formal definition for NP-completeness==


2 Richmond, Bettina (1999-01-12). "Area of a Circle". Western Kentucky University. Retrieved on 2007-11-04.
Although there are many equivalent ways of describing '''NP'''-completeness, in the context of the '''P''' vs '''NP''' question, it is best to define '''NP'''-complete problems in terms of '''NP''' problems.{{Fact|date=January 2008}}
3Amulya Kumar Bag. Binomial Theorem in Ancient India. Indian J.History Sci.,1:68-74,1966.


4 "Neither Newton nor Leibniz - The Pre-History of Calculus and Celestial Mechanics in Medieval Kerala". MAT 314. Canisius College. Retrieved on
Let <math>\ L</math> be a language over a finite alphabet <math>\ \Sigma</math>.
2006-07-09.


5William E. Boyce and Richard C. DiPrima, Elementary Differential Equations and Boundary Value Problems, Eighth edition. John Wiley & Sons,
<math>\ L</math> is '''NP'''-complete if, and only if, the following two conditions are satisfied:
Inc., New Jersey, 2005. ISBN 0-471-43338-1


6Bell, John L., The Art of the Intelligible: An Elementary Survey of Mathematics in its Conceptual Development, Kluwer, 1999. ISBN 0-7923-5972-
#<math>L\in\mathbf{NP}</math>; and
0.
#any <math>L^{'}\in\mathbf{NP}</math> is polynomial time reducible to <math>\ L</math> (written as <math>L^{'}\leq_{p} L</math>), where <math>L^{'}\leq_{p} L</math> if, and only if, the following two conditions are satisfied:
##There exists <math>f : \Sigma^{*}\rightarrow\Sigma^{*}</math> such that <math>
\forall w\in\Sigma^{*}(w\in L^{'}\Leftrightarrow f(w)\in L)</math>; and
##there exists a polynomial time Turing machine which halts with <math>\ f(w)</math> on its tape on any input <math>\ w</math>.


7Heaton, H. (1896) A Method of Solving Quadratic Equations, American Mathematical Monthly 3(10), 236–237.
==Still harder problems==
{{See also|Complexity class}}


Through the Quadratic Equation [7]:.
Although it is unknown whether '''P''' = '''NP''', problems outside of '''P''' are known. A number of [[succinct problem]]s, that is, problems which operate not on normal input but on a computational description of the input, are known to be [[EXPTIME|'''EXPTIME'''-complete]]. Because it can be shown that '''P''' <math>\subsetneq</math> '''[[EXPTIME]]''', these problems are outside '''P''', and so require more than polynomial time. In fact, by the [[time hierarchy theorem]], they cannot be solved in significantly less than exponential time.{{Fact|date=January 2008}}
..=
-..± ..2-4....
2..
To be succeed by a modified Taylor Series Expansion [8];.
....=1+
..
1!
+
..22!
+
..33!
+.,-8<..<8


The problem of deciding the truth of a statement in [[Presburger arithmetic]] requires even more time. Fischer and [[Michael O. Rabin|Rabin]] proved in 1974 that every algorithm which decides the truth of Presburger statements has a runtime of at least <math>2^{2^{cn}}</math> for some constant ''c''. Here, ''n'' is the length of the Presburger statement. Hence, the problem is known to need more than exponential run time. Even more difficult are the [[List of undecidable problems|undecidable problems]], such as the [[halting problem]]. They cannot be completely solved by any algorithm, in the sense that for any particular algorithm there is at least one input for which that algorithm will not produce the right answer; it will either produce the wrong answer, finish without giving a conclusive answer, or otherwise run forever without producing any answer at all.


Martin M. Musatov, Los Angeles, CA, m.mm@vzw.blackberry.net tel: 818.430.4586
== Does NP mean "hard" and P mean "easy"? ==
Feel free to contact me for a complete computational demonstration of polynomial time achievement.
[[Image:KnapsackEmpComplexity.GIF|thumb|310 px|The graph shows time (average of 100 instances in msec using a 933 MHz Pentium III) vs.problem size for knapsack problems for a state-of-the-art specialized algorithm. Quadratic fit suggests that empirical algorithmic complexity for instances with 50–10,000 variables is O((log&nbsp;''n'')<sup>2</sup>). The data comes from <ref name=Pisinger2003>Pisinger, D. 2003. "Where are the hard knapsack problems?" Technical Report 2003/08, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark</ref>]]
All of the above discussion has assumed that '''P''' means "easy" and "not in '''P'''" means "hard". This assumption, known as ''[[Cobham's thesis]]'', though a common and reasonably accurate assumption in complexity theory, is not always true in practice; the size of constant factors or exponents may have practical importance, or there may be solutions that work for situations encountered in practice despite having poor worst-case performance in theory (this is the case for instance for the simplex algorithm in [[linear programming]]). Other solutions violate the Turing machine model on which '''P''' and '''NP''' are defined by introducing concepts like randomness and quantum computation.


Because of these factors, even if a problem is shown to be NP-complete, and even if '''P''' &ne; '''NP''', there may still be effective approaches to tackling the problem in practice. There are algorithms for many NP-complete problems, such as the [[knapsack problem]], the [[travelling salesman problem]] and the [[boolean satisfiability problem]], that can solve to optimality many real-world instances in reasonable time. The empirical average complexity (time vs. problem size) of such algorithms can be surprisingly low.


8Whittaker, E. T. and Watson, G. N. "Forms of the Remainder in Taylor's Series." §5.41 in A Course in Modern Analysis, 4th ed. Cambridge,
==Why many computer scientists think P ≠ NP==
England: Cambridge University Press, pp. 95-96, 1990.
Posted by martinmusatov at 12:46 AM 0 comments
Tuesday, February 24, 2009


Posted by martinmusatov at 7:47 AM 2 comments
Most computer scientists believe that '''P'''≠'''NP'''. A key reason for this belief is that after decades of studying these problems, no one has been able to find a polynomial-time algorithm for any of more than 3000 important known '''NP'''-complete problems (see [[List of NP-complete problems]]). These algorithms were sought long before the concept of '''NP'''-completeness was even known ([[Karp's 21 NP-complete problems]], among the first found, were all well-known existing problems at the time they were shown to be NP-complete). Furthermore, the result '''P''' = '''NP''' would imply many other startling results that are currently believed to be false, such as '''NP''' = '''[[co-NP]]''' and '''P''' = [[PH (complexity)|'''PH''']].
Wednesday, December 19, 2007
Space
What is outside space? What can be outside space, but more space?


Light creates matter. Light creates space.
It is also intuitively argued that the existence of problems that are hard to solve but for which the solutions are easy to verify matches real-world experience.<ref>{{cite web |url=http://scottaaronson.com/blog/?p=122 |author=Scott Aaronson |title=Reasons to believe}}, point 9.</ref>
{{quote|If P<nowiki>=</nowiki>NP, then the world would be a profoundly different place than we usually assume it to be. There would be no special value in “creative leaps,” no fundamental gap between solving a problem and recognizing the solution once it’s found. Everyone who could appreciate a symphony would be Mozart; everyone who could follow a step-by-step argument would be Gauss...| [[Scott Aaronson]], [[MIT]]}}


"The relationship between the Sun and the Earth is illustrative of the relationship between light and matter, a relationship so complex that we struggle, with Teilhard de Chardin, to grasp its full and subtle import. Light creates matter (in the Big Bang), and subsequently (as in the Sun-Earth interaction) produces life, consciousness, and the abstract information systems of human thought and technology. At its root, every human achievement, action, emotion, or idea is nothing else but the metamorphosis of light. (See: "The Sun Tetrahedron") and "The Sun Archetype".
On the other hand, some researchers believe that we are overconfident in '''P''' ≠ '''NP''' and should explore proofs of '''P''' = '''NP''' as well. For example, in 2002 these statements were made:<ref name="poll" />
{{quote|The main argument in favour of '''P'''≠'''NP''' is the total lack of fundamental progress in the area of exhaustive search. This is, in my opinion, a very weak argument. The space of algorithms is very large and we are only at the beginning of its exploration. [. . .] The resolution of [[Fermat's Last Theorem]] also shows that very simply [''sic''] questions may be settled only by very deep theories.|[[Moshe Y. Vardi]], [[Rice University]]}}
{{quote|Being attached to a speculation is not a good guide to research planning. One should always try both directions of every problem. Prejudice has caused famous mathematicians to fail to solve famous problems whose solution was opposite to their expectations, even though they had developed all the methods required.|[[Anil Nerode]], [[Cornell University]]}}


"The development, through evolutionary time, of massive, complex, information structures can be understood not only in terms of matter's quest for its lost antimatter partners and a return to its original state of light-symmetry, but also as matter's quest for the original connectivity and unity of the light Universe, which knew no division or separation, and which communicated freely throughout its whole structure instantaneously."
==Consequences of proof==


I read this here: http://people.cornell.edu/pages/jag8/commentary.html
One of the reasons the problem attracts so much attention is the consequences of the answer.
It amazed me.
Posted by martinmusatov at 5:26 PM 2 comments
Space
What is outside space? What can be outside space, but more space?


Light creates matter. Light creates space.
A proof of '''P''' = '''NP''' could have stunning practical consequences, if the proof leads to efficient methods for solving some of the important problems in NP. Various NP-complete problems are fundamental in many fields. There are enormous positive consequences that would follow from rendering tractable many currently mathematically intractable problems. For instance, many problems in [[operations research]] are NP-complete, such as some types of [[integer programming]], and the [[travelling salesman problem]], to name two of the most famous examples. Efficient solutions to these problems would have enormous implications for [[logistics]]. Many other important problems, such as some problems in [[Protein structure prediction]] are also '''NP'''-complete;<ref name="Berger">{{cite journal |author=Berger B, Leighton T |title=Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete |journal=J. Comput. Biol. |volume=5 |issue=1 |pages=27–40 |year=1998 |pmid=9541869 |doi=10.1145/1052796.1052804}}</ref> if these problems were efficiently solvable it could spur considerable advances in biology.


"The relationship between the Sun and the Earth is illustrative of the relationship between light and matter, a relationship so complex that we struggle, with Teilhard de Chardin, to grasp its full and subtle import. Light creates matter (in the Big Bang), and subsequently (as in the Sun-Earth interaction) produces life, consciousness, and the abstract information systems of human thought and technology. At its root, every human achievement, action, emotion, or idea is nothing else but the metamorphosis of light. (See: "The Sun Tetrahedron") and "The Sun Archetype".
But such changes may pale in significance compared to the revolution an efficient method for solving NP-complete problems would cause in mathematics itself. According to [[Stephen Cook]],<ref name="Official Problem Description">{{cite web|author=Stephen Cook|title=The P versus NP Problem|url=http://www.claymath.org/millennium/P_vs_NP/Official_Problem_Description.pdf|accessdate=2007-08-27|format=PDF}}</ref>


"The development, through evolutionary time, of massive, complex, information structures can be understood not only in terms of matter's quest for its lost antimatter partners and a return to its original state of light-symmetry, but also as matter's quest for the original connectivity and unity of the light Universe, which knew no division or separation, and which communicated freely throughout its whole structure instantaneously."
{{quote|...it would transform mathematics by allowing a computer to find a formal proof of any theorem which has a proof of a reasonable length, since formal proofs can easily be recognized in polynomial time. Example problems may well include all of the [[Clay Math Institute#Millennium Prize Problems|CMI prize problems]].}}


I read this here: http://people.cornell.edu/pages/jag8/commentary.html
Research mathematicians spend their careers trying to prove theorems, and some proofs have taken decades or even centuries to find after problems have been stated – for instance, [[Fermat's Last Theorem]] took over three centuries to prove. A method that is guaranteed to find proofs to theorems, should one exist of a "reasonable" size, would essentially end this struggle.


It amazed me.
A proof that showed that '''P''' ≠ '''NP''', while lacking the practical computational benefits of a proof that '''P''' = '''NP''', would also represent a massive advance in computational complexity theory and provide guidance for future research. It would allow one to show in a formal way that many common problems cannot be solved efficiently, so that the attention of researchers can be focused on partial solutions or solutions to other problems. Due to widespread belief in '''P''' ≠ '''NP''', much of this focusing of research has already taken place.<ref>{{cite journal |title=The Heuristic Problem-Solving Approach |author=L. R. Foulds |journal=The Journal of the Operational Research Society |volume=34 |issue=10 |month=October | year=1983 |pages=927–934 |url=http://www.jstor.org/pss/2580891 |doi=10.2307/2580891}}</ref>
Posted by martinmusatov at 5:12 PM 2 comments


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==Results about difficulty of proof==
If it is known that if any NP-complet...
from The Solution to P=NP and The Riemann Hypothesis.
If it is known that if any NP-complet...
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PversusNP
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from The Solution to P Versus NP and The Riemann Hypo
The Clay Mathematics Institute million-dollars prize and a huge amount of dedicated research with no substantial results suggest that the problem is difficult. In fact, some of the most fruitful research related to the '''P''' = '''NP''' problem has been in showing that existing proof techniques are not powerful enough to answer the question, thus suggesting that novel technical approaches are probably required.
PversusNP
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Essentially all known proof techniques in [[computational complexity]] theory fall into one of the following classifications, each of which is known to be insufficient to prove that '''P''' ≠ '''NP''':
P-NP
Light-Symmetry
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* '''Relativizing proofs:''' Imagine a world where every algorithm is allowed to make queries to some fixed subroutine called an [[oracle machine|oracle]], and the running time of the oracle is not counted against the running time of the algorithm. Most proofs, especially classical ones, apply uniformly in a world with oracles, regardless of what the oracle does. These proofs are called ''relativizing''. In 1975, Baker, Gill, and Solovay showed that '''P''' = '''NP''' with respect to some oracles, while '''P''' ≠ '''NP''' for other oracles.<ref>T. P. Baker, J. Gill, R. Solovay. ''Relativizations of the P =? NP Question''. [[SIAM Journal on Computing]], 4(4): 431-442 (1975)</ref> Since relativizing proofs can only prove statements that are uniformly true with respect to all possible oracles, this showed that relativizing techniques cannot resolve '''P''' = '''NP'''.
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* '''Natural proofs:''' In 1993, [[Alexander Razborov]] and [[Steven Rudich]] defined a general class of proof techniques for circuit complexity lower bounds, called ''[[natural proof]]s''. At the time, all previously known circuit lower bounds were natural, and circuit complexity was considered a very promising approach for resolving '''P''' = '''NP'''. However, Razborov and Rudich showed that in order to prove '''P''' ≠ '''NP''' using a natural proof, one necessarily must also prove an even stronger statement, which is believed to be false. Thus it is unlikely that natural proofs alone can resolve '''P''' = '''NP'''.

* '''Algebrizing proofs:''' After the Baker-Gill-Solovay result, new non-relativizing proof techniques were successfully used to prove that [[IP (complexity)|IP]] = [[PSPACE]]. However, in 2008, [[Scott Aaronson]] and [[Avi Wigderson]] showed that the main new technical tool used in the '''IP''' = '''PSPACE''' proof, which they called ''algebrization'', was also insufficient to resolve '''P''' = '''NP'''.<ref>S. Aaronson and A. Wigderson. Algebrization: A New Barrier in Complexity Theory, in Proceedings of ACM STOC'2008, pp. 731-740.</ref>

These barriers are another reason why '''NP'''-complete problems are useful: if a polynomial-time algorithm can be demonstrated for an '''NP'''-complete problem, this would solve the '''P''' = '''NP''' problem in a way which is not excluded by the above results.

==Polynomial-time algorithms==

No one knows whether polynomial-time algorithms exist for '''NP'''-complete languages. But if such algorithms do exist, some of them are already known. For example, the following algorithm (due to Levin) correctly accepts an '''NP'''-complete language, but as of 2008, it is unknown how long it takes in general.

// Algorithm that accepts the NP-complete language [[subset sum problem|SUBSET-SUM]].
//
// This is a polynomial-time algorithm if and only if '''P'''='''NP'''.
//
// "Polynomial-time" means it returns "yes" in polynomial time when
// the answer should be "yes", and runs forever when it is "no".
//
// Input: S = a finite set of integers
// Output: "yes" if any subset of S adds up to 0.
// Runs forever with no output otherwise.
// Note: "Program number P" is the program obtained by
// writing the integer P in binary, then
// considering that string of bits to be a
// program. Every possible program can be
// generated this way, though most do nothing
// because of syntax errors.<br />
FOR N = 1...infinity
FOR P = 1...N
Run program number P for N steps with input S
IF the program outputs a list of distinct integers
AND the integers are all in S
AND the integers sum to 0<br />
THEN
OUTPUT "yes" and HALT

If, and only if, '''P''' = '''NP''', then this is a polynomial-time algorithm accepting an '''NP'''-complete language. "Accepting" means it gives "yes" answers in polynomial time, but is allowed to run forever when the answer is "no".

Perhaps we want to "solve" the SUBSET-SUM problem, rather than just "accept" the SUBSET-SUM language. That means we want the algorithm to always halt and return a "yes" or "no" answer. {{As of|2008}}, it is unknown whether an algorithm exists that does this in polynomial time. But if there is an algorithm that provably does this in polynomial time, then so does the algorithm that is obtained by replacing the IF statement in the above algorithm with this:

IF the program outputs a complete math proof
AND each step of the proof is legal
AND the conclusion is that S does (or does not) have a subset summing to 0
THEN
OUTPUT "yes" (or "no") and HALT

==Logical characterizations==

The '''P''' = '''NP''' problem can be restated in terms of the expressibility of certain classes of logical statements, as a result of work in [[descriptive complexity]]. All languages (of finite structures with a fixed [[signature (logic)|signature]] including a [[linear order]] relation) in '''P''' can be expressed in [[first-order logic]] with the addition of a suitable [[least fixed point]] operator (effectively, this, in combination with the order, allows the definition of recursive functions); indeed, (as long as the signature contains at least one predicate or function in addition to the distinguished order relation [so that the amount of space taken to store such finite structures is actually polynomial in the number of elements in the structure]), this precisely characterizes '''P'''. Similarly, '''NP''' is the set of languages expressible in existential [[second-order logic]] &mdash; that is, second-order logic restricted to exclude [[universal quantification]] over relations, functions, and subsets. The languages in the [[polynomial hierarchy]], '''[[PH (complexity)|PH]]''', correspond to all of [[second-order logic]]. Thus, the question "is '''P''' a proper subset of '''NP'''" can be reformulated as "is existential second-order logic able to describe languages (of finite linearly ordered structures with nontrivial signature) that first-order logic with least fixed point cannot?". The word "existential" can even be dropped from the previous characterization, since '''P''' = '''NP''' if and only if '''P''' = '''PH''' (as the former would establish that '''NP''' = '''co-NP''', which in turn would imply that '''NP''' = '''PH''').

==See also==

*[[P (complexity)|'''P''' (complexity)]]
*[[NP (complexity)|'''NP''' (complexity)]]
*[[NP-complete|'''NP'''-complete]]
*[[Game complexity]]
*[[Cobham's thesis]]
*[[List of open problems in computer science]]
*[[Unsolved problems in mathematics]]

==References==

{{reflist}}

==Further reading==

* A. S. Fraenkel and D. Lichtenstein, Computing a perfect strategy for n*n chess requires time exponential in n, Proc. 8th Int. Coll. ''Automata, Languages, and Programming'', Springer LNCS 115 (1981) 278–293 and ''J. Comb. Th. A'' 31 (1981) 199–214.
* E. Berlekamp and D. Wolfe, Mathematical Go: Chilling Gets the Last Point, A. K. Peters, 1994. D. Wolfe, Go endgames are hard, MSRI Combinatorial Game Theory Research Worksh., 2000.
* [[Neil Immerman]]. Languages Which Capture Complexity Classes. ''15th ACM STOC Symposium'', pp.347–354. 1983.
* {{cite book|author=[[Thomas H. Cormen]], [[Charles E. Leiserson]], [[Ronald L. Rivest]], and [[Clifford Stein]]|title=[[Introduction to Algorithms]]|edition=Second Edition|publisher=MIT Press and McGraw-Hill|year=2001|isbn=0-262-03293-7|chapter = Chapter 34: NP-Completeness|pages = pp.966–1021}}
* {{cite book|author=[[Christos Papadimitriou]]|year=1993|title=Computational Complexity|publisher=Addison Wesley|edition=1st edition|isbn = 0-201-53082-1|chapter=Chapter 14: On P vs. NP|pages=pp.329–356}}

==External links==
* [http://www.claymath.org/millennium/ The Clay Mathematics Institute Millennium Prize Problems]
* {{PDF|[http://www.claymath.org/millennium/P_vs_NP/Official_Problem_Description.pdf The Clay Math Institute Official Problem Description]|118&nbsp;KB}}
*[http://www.claymath.org/Popular_Lectures/Minesweeper/ Ian Stewart on Minesweeper as '''NP'''-complete at The Clay Math Institute]
* Gerhard J. Woeginger. [http://www.win.tue.nl/~gwoegi/P-versus-NP.htm The P-versus-NP page]. A list of links to a number of purported solutions to the problem. Some of these links state that P equals NP, some of them state the opposite. It is probable that all these alleged solutions are incorrect.
* [http://www.ics.uci.edu/~eppstein/cgt/hard.html Computational Complexity of Games and Puzzles]
*{{CZoo|Class P|P#p}}, {{CZoo|Class NP|N#np}}
* [http://www.qeden.com/ Qeden], a wiki that aims to solve the Millennium Prize Problems ("down for maintenance" as of 15th of July 2008)
* [http://scottaaronson.com/blog/?p=122 Scott Aaronson's Shtetl Optimized blog: Reasons to believe], a list of justifications for the belief that P ≠ NP

{{ComplexityClasses}}

[[Category:Structural complexity theory]]
[[Category:Mathematical optimization]]
[[Category:Conjectures]]
[[Category:Unsolved problems in mathematics]]
[[Category:Unsolved problems in computer science]]
[[Category:Millennium Prize Problems]]

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[[cs:Problém P versus NP]]
[[de:P-NP-Problem]]
[[es:Problema ¿P=NP?]]
[[eo:Demando P = NP]]
[[ko:P-NP 문제]]
[[it:Classi di complessità P e NP]]
[[he:P=NP]]
[[ja:P≠NP予想]]
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[[ru:Равенство классов P и NP]]
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Revision as of 02:38, 28 February 2009

The Solution to P Versus NP and The Riemann Hypothesis. The definitive solutions to both the Riemann Hypothesis and P Versus NP.

Wednesday, February 25, 2009 PversusNP Indeed, Mr. Musatov's work is correct and I do hereby formally acknowledge and verify the correctness of his work and openly acknowledge him as amongst out my peers. The day it will be said it was today I have seen it and will state whole it has been proven yes that good God indeed, P equals NP.--Signed Stephen A. Cook, (Congratulations, Martin)

Stephen A. Cook Distinguished University Professor Department of Computer Science University of Toronto Toronto, Canada M5S 3G4

Tel: (416) 978-5183 sacook [at] cs [dot] toronto [dot] edu

For press inquiries, please contact: James Carlson or David Ellwood, (617) 995 2600, Email: carlson [at] claymath.org; ellwood [at] claymath.org.


NP: Nondeterministic Polynomial-Time 02.24.2009 (→P = ⋯⋮⋱⋮⋯ N ⋯⋮⋱⋮⋯ P) http://qwiki.stanford.edu/wiki/Complexity_Zoo:N#np Proof: published (M.M.M) If it is known that if any NP-complete language is sparse (contains no more than a polynomial number of strings of length n), then P = NP. [BH08] improved this result, showing that if any language in NP has an NP-hard set of subexponential density, then coNP is contained in NP/poly and thus, by [Yap82], PH collapses to the third level. Conceptually, a decision problem is a problem that takes as input some string, and outputs "yes" or "no". If there is an algorithm (say a Turing machine, or a computer program with unbounded memory) which is able to produce the correct answer for any input string of length Failed to parse (<math_output_error>): n in at most c \cdot n^k steps, where k and Failed to parse (<math_output_error>): c are constants independent of the input string, then we say that the problem can be solved in polynomial time and we place it in the class P. Formally, P is defined as the set of all languages which can be decided by a deterministic polynomial-time Turing machine. That is, P = {L:L = L(M) for some deterministic polynomial-time Turing machine M} where L(M) = \{ w\in\Sigma^{*}: M \text{ accepts } w \} and a deterministic polynomial-time Turing machine is a deterministic Turing machine M which satisfies the following two conditions: 1. M halts on all input w; and 2. there exists k \in N such that T_{M}(n)\in\; O(nk), where T_{M}(n) = \max\{ t_{M}(w) : w\in\Sigma^{*}, \leftw\right = n \} and tM(w) = number of steps M takes to halt on input w. NP can be defined similarly using nondeterministic Turing machines (the traditional way). However, a modern approach to define NP is to use the concept of certificate and verifier. Formally, NP is defined as the set of languages over a finite alphabet that have a verifier that runs in polynomial time, where the notion of "verifier" is defined as follows. Let Failed to parse (<math_output_error>): L be a language over a finite alphabet, Σ. L\in\mathbf{NP} if, and only if, there exists a binary relation R\subset\Sigma^{*}\times\Sigma^{*} and a positive integer k such that the following two conditions are satisfied: 1. For all x\in\Sigma^{*}, x\in L \Leftrightarrow\exists y\in\Sigma^{*} such that (x,y)\in R\; and \lefty\right\in\;O(\leftx\right^{k}); and 2. the language L_{R} = \{ x\# y:(x,y)\in R\} over \Sigma\cup\{\#\} is decidable by a Turing machine in polynomial time. A Turing machine that decides LR is called a verifier for L and a y such that (x,y)\in R is called a certificate of membership of x in L. In general, a verifier does not have to be polynomial-time. However, for L to be in NP, there must be a verifier that runs in polynomial time. --MartinMichaelMusatov 07:18, 24 February 2009 (UTC) [edit] NPC: NP-Complete The class of decision problems such that (1) they're in NP and (2) every problem in NP is reducible to them (under some notion of reduction). In other words, the hardest problems in NP. Two notions of reduction from problem A to problem B are usually considered: Karp or many-one reductions. Here a polynomial-time algorithm is given as input an instance of problem A, and must produce as output an instance of problem B. Turing reductions, in this context also called Cook reductions. Here the algorithm for problem B can make arbitrarily many calls to an oracle for problem A.Some examples of NP-complete problems are discussed under the entry for NP. The classic reference on NPC is [GJ79]. Unless P = NP, NPC does not contain any sparse problems: that is, problems such that the number of 'yes' instances of size n is upper-bounded by a polynomial in n [Mah82]. A famous conjecture [BH77] asserts that all NP-complete problems are polynomial-time isomorphic -- i.e. between any two problems, there is a one-to-one and onto Karp reduction. If that's true, the NP-complete problems could be interpreted as mere "relabelings" of one another. NP-complete problems are p-superterse unless P = NP [BKS95]. This means that, given k Boolean formulas F1,...,Fk, if you can rule out even one of the 2k possibilities in polynomial time (e.g., "if F1,...,Fk-1 are all unsatisfiable then Fk is satisfiable"), then P = NP. [BH08] H. Buhrman and J. Hitchcock. NP-Hard sets are exponentially eense unless NP is contained in coNP/poly, Electronic Colloquium on Computational Complexity, ECCC Report TR08-022, accepted on Mar 11, 2008. http://eccc.hpi-web.de/eccc-reports/2008/TR08-022/index.html [Yap83] C. Yap. Some consequences of non-uniform conditions on uniform classes, Theoretical Computer Science, (1983), 26, 287-300. [GJ79] M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman, 1979. [Mah82] S. R. Mahaney. Sparse complete sets for NP: Solution of a conjecture by Berman and Hartmanis, Journal of Computer and System Sciences 25:130-143, 1982. [BH77] L. Berman and J. Hartmanis. On isomorphism and density of NP and other complete sets, SIAM Journal on Computing 6:305-322, 1977. [BKS95] R. Beigel, M. Kummer, and F. Stephan. Approximable sets, Information and Computation 120(2):304-314, 1995. http://www.cis.temple.edu/~beigel/papers/bks-queries2-ic.PS.gz.

BinaryChaosTheory.jpg

i ...... .. ............ .... ................ MARTIN M. MUSATOV An Open Address to Mr. Stephen A. Cook

(r=0) 1. STATEMENT OF THE SOLUTION



T


his solution to P versus NP explains how every language accepted by some non deterministic algorithm in polynomial time can be accepted by some (deterministic) algorithm in polynomial time. The central point in polar coordinates, or the point with all zero coordinates (0, ..., 0) in Cartesian coordinates is the binary point of origin [1]: .


In three dimensions, the x-axis, y-axis, and z-axis meet at the origin.


The matrix form for these coordinates may be expressed: The derivatives of the unit vectors are then:



To formally define the solution it is indeed necessary to observe the model of a computer, or Turing machine and process information in “real-time” as it is received as a computable function or linear stream. By this declaration, formally, the class P contains the indecision problems P = . ... . N . ... . P From this point, we can continue the expansion: The area of a circle [2]: . ..=....2 The binomial theorem [3]: ..+.. ..= .. .. ........-.. .. ..=0 Expansion of a sum (Taylor Series) [4];. 1+.. ..=1+ .... 1! + .. ..-1 ..22! +. Followed by the Fourier Series [5]:. .. .. =..0+ ....cos ...... .. +....sin ...... .. 8 ..=1 The Pythagorean Formula [6]:. ..2+..2=..2



1 Arfken, G. "Special Coordinate Systems--Rectangular Cartesian Coordinates." §2.3 in Mathematical Methods for Physicists, 3rd ed. Orlando, FL: Academic Press, pp. 94-95, 1985.


2 Richmond, Bettina (1999-01-12). "Area of a Circle". Western Kentucky University. Retrieved on 2007-11-04. 3Amulya Kumar Bag. Binomial Theorem in Ancient India. Indian J.History Sci.,1:68-74,1966.

4 "Neither Newton nor Leibniz - The Pre-History of Calculus and Celestial Mechanics in Medieval Kerala". MAT 314. Canisius College. Retrieved on 2006-07-09.

5William E. Boyce and Richard C. DiPrima, Elementary Differential Equations and Boundary Value Problems, Eighth edition. John Wiley & Sons, Inc., New Jersey, 2005. ISBN 0-471-43338-1

6Bell, John L., The Art of the Intelligible: An Elementary Survey of Mathematics in its Conceptual Development, Kluwer, 1999. ISBN 0-7923-5972- 0.

7Heaton, H. (1896) A Method of Solving Quadratic Equations, American Mathematical Monthly 3(10), 236–237.

Through the Quadratic Equation [7]:. ..= -..± ..2-4.... 2.. To be succeed by a modified Taylor Series Expansion [8];. ....=1+ .. 1! + ..22! + ..33! +.,-8<..<8


Martin M. Musatov, Los Angeles, CA, m.mm@vzw.blackberry.net tel: 818.430.4586 Feel free to contact me for a complete computational demonstration of polynomial time achievement.


8Whittaker, E. T. and Watson, G. N. "Forms of the Remainder in Taylor's Series." §5.41 in A Course in Modern Analysis, 4th ed. Cambridge, England: Cambridge University Press, pp. 95-96, 1990. Posted by martinmusatov at 12:46 AM 0 comments Tuesday, February 24, 2009

Posted by martinmusatov at 7:47 AM 2 comments Wednesday, December 19, 2007 Space What is outside space? What can be outside space, but more space?

Light creates matter. Light creates space.

"The relationship between the Sun and the Earth is illustrative of the relationship between light and matter, a relationship so complex that we struggle, with Teilhard de Chardin, to grasp its full and subtle import. Light creates matter (in the Big Bang), and subsequently (as in the Sun-Earth interaction) produces life, consciousness, and the abstract information systems of human thought and technology. At its root, every human achievement, action, emotion, or idea is nothing else but the metamorphosis of light. (See: "The Sun Tetrahedron") and "The Sun Archetype".

"The development, through evolutionary time, of massive, complex, information structures can be understood not only in terms of matter's quest for its lost antimatter partners and a return to its original state of light-symmetry, but also as matter's quest for the original connectivity and unity of the light Universe, which knew no division or separation, and which communicated freely throughout its whole structure instantaneously."

I read this here: http://people.cornell.edu/pages/jag8/commentary.html It amazed me. Posted by martinmusatov at 5:26 PM 2 comments Space What is outside space? What can be outside space, but more space?

Light creates matter. Light creates space.

"The relationship between the Sun and the Earth is illustrative of the relationship between light and matter, a relationship so complex that we struggle, with Teilhard de Chardin, to grasp its full and subtle import. Light creates matter (in the Big Bang), and subsequently (as in the Sun-Earth interaction) produces life, consciousness, and the abstract information systems of human thought and technology. At its root, every human achievement, action, emotion, or idea is nothing else but the metamorphosis of light. (See: "The Sun Tetrahedron") and "The Sun Archetype".

"The development, through evolutionary time, of massive, complex, information structures can be understood not only in terms of matter's quest for its lost antimatter partners and a return to its original state of light-symmetry, but also as matter's quest for the original connectivity and unity of the light Universe, which knew no division or separation, and which communicated freely throughout its whole structure instantaneously."

I read this here: http://people.cornell.edu/pages/jag8/commentary.html

It amazed me. Posted by martinmusatov at 5:12 PM 2 comments

Subscribe to: Posts (Atom) Mshare If it is known that if any NP-complet... from The Solution to P=NP and The Riemann Hypothesis. If it is known that if any NP-complet... from The Solution to P=NP and The Riemann Hypothesis. PversusNP from The Solution to P=NP and The Riemann Hypothesis.

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P-NP 

Light-Symmetry

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