mm-1809 I don't know how to proceed to get every negative rational number in the the image of Q by f. David I have a n-dimensional point and m constraints as shown below; Point P= (p1,p2,...pN) Constraint 1: c1*p1+c2*p2+...+C < 0 ... Constraint m: z1*p1+z2*p2+...+Z < 0 Note: Any Constraint can be viewed as a normalized hyperplane defined on the n-dimensional space. It can be argued that, m constraints define a surface (closed or not) on the n-dimensional space. Suppose that Point P does not satify the constraints. I wonder, how can I find the smallest distance between Point P to the surface determined by the constraints. Any idea ? Example: in 2-D Point P= (-1,+1) Const2: p2 < 0 It is clear that X does not satify both Const1 and 2. For this case the distance between Point P to an area (defined by the constraints) can easly computed such that Distance= sqrt(p1*p1+p2*p2) It is euclidean distance. However this not always true. | | P | | ---------------- p1 plane | | An Area where all | | p2 plane Consider the 2- dimensional case: each constraint defines a half-plane. Assuming they intersect at all (so there is a solution), this will define a region R bordered by a convex region, which has vertices at some of the points of intersections of the lines in the constraints. These points can be easily soved for, and the ones outside R (all points will be either on the boundry of R or inside) can be discarded (check the equations to see which ones fail to satisfy them). Now, get the distance from P to each vertex. For sake of discussion, assume that the smallest distance is to vertex v2, with adjacent vertices v1 and v3, defining lines L1 and L3. Find the projections onto L1 and L3 of P (normal projections, of course). If this point lies on R (and at most 1 of these points will do so), then that is the closest point (since R is convex), and you have the distance. If neither of these projections lie on R, then v1 is the closest point. This should translate naturally into more dimensions, checking distances to the vertices of the bounded region, and using the adjacent hyperplanes to v1 for finding the distances. If there aren't enough smallest-dimensional spaces in the intesections. (in the above example, the z-axis is the intersection of the planes, so you would use the distance from P to the z-axis, and the distance from z to the xz and yz planes.) Consider a set of intersecting halfspaces with vertices v1=(0,5), v2=(0,-4), v3=(1,0) with P= (-1,0) If I understand your proposed algorithm properly, v3 would be identified as the nearest vertex, the projections of P to halfspaces defined by v1,v3 and v2,v3 would both lie on R, but neither of these projections nor v3 is the closest point (which by inspection is (0,0)) A fix to the proposed algorithm is to consider only vertices that come from half-spaces that do not contain P. In this case, only vertices v1 and v2 would be considered, v2 would be the closest, and the closest point to P is found by projecting to the plane v1,v2. -Rick such an assumpiton, due to the nature of the problem. If the half-planes do not intersect, then there is no region (in more than 2 dimensions no hypersurface) defined, and so you need to rephrase the problem. For example, what if I choose P to be the origin, and give What answer would you want to get here? By the way the problem is phrased, there is no answer, as the restrictions leave you with an empty set. Note that since the vertices I look for are the solutions set of your constraint equations (using equality, rather than inequality), the assumption that the regions intersect is the same as assumingthat the system of equations is consistant. If there is no intersection, then this will be found before any distances are calculated. Now, let me show you the graph of your question. R is the surface where all the constrainsts are met. Now the minimum distance between P to region R is 1. | | | R | | Region| | | ------------------------------------- P(0,0) | | | | | | | | | | x=1 x=2 Numerically, you can do it this way, with mathematica (3D example) : pointP = {2,3,4}; pointX = {x,y,z}; distance = Sqrt[(pointP-pointX).(pointP-pointX)]; constraints = x+y+z <= 1 && x+2y+z <= 2; NMinimize[{distance, constraints}, pointX] OP: Easier: minimize the square of the distance. Astanoff points out that this is a nonlineanr programming problem with constraints. This particular example is easy, of course: You have a quadratic objective you know that the closest point lies on at least one boundary. Thus, you have three cases to check: only one constraint tight (two cases) or both tight. Use the Kuhn-Tucker-Karesh conditions. There is a vast literature on nonlinear programming. Luenberger has written an elementary introductory text (on both linear and nonlinear programming). -- Stephen J. Herschkorn sjherschko@netscape.net I think, you clearly understand what i mean. However, I wonder the algorithm. Is there a faster way to compute the minimum distance ? I suppose, it can be solved much simpler and more fast than any other Optimization problems. Of course, I hope so. Because, the constraints are normalized. I've got a question concerning notation. Given a binary vector xi with i=1,...,n: how can I show with a notation symbol that I just want to address a part of the vector, for example the elements x3,...,x5 of this vector? How can I for example formally express F(x3,...,x5)=1? Maybe you can sent me an email with an attachment, if it's too difficult to show the solution with the ASCII symbols. On 3 Mar 2005 04:21:32 -0800, christoph.stich@gmx.de (Christoph) etc. so your vector is sum[k = 1 .. n] x_k * e_k you could let M = { 3, 4, 5} and write y = sum[ k in M] x_k * e_k. I'm not sure if that is what you mean by addressing those parts of the vector. --Lynn Why is it so? It does not seem obvious to me. David | |Why is it so? It does not seem obvious to me. assuming that i'm not making some stupid mistake, it's pretty obvious step-by-step even if not truly obvious all in one first glance. do if you like, you can try testing it out on some examples where c and d are categories each with just a few objects and a few morphisms. -- [e-mail address jdolan@math.ucr.edu] rlankine@hotmail.com says... Risto, Just an observation - what would your reaction be if you finally found a way to generate the string from the number (with a method that allowed simple quick processing of very large numbers)? If I understand correctly a fair proportion of the encryption techniques currently used (and believed to be uncrackable in a useful time period) rely on the difficulty and time-consumption of factorising very large numbers. You would therefore be in possession of an algorithm that could do one of two things for you personally: 1. Fame and prestige by publication - we are talking front page of Nature and Time etc 2. Great wealth by selling (secretly) to the highest bidder. These are mutually exclusive - as soon as the world knows that large numbers can be factorised easily (even if the method is not published) those that need real security will stop using them to encrypt and thus nobody would pay that highly for your algorithm. So, do you provide for yourself, your family etc to live in luxury and ease and damn your prinicples or do you publish? Matthew Newell who would probably give the NSA a single year licence and live on the proceeds, pray no-one else discovered in that year and then publish. Alas, because the fear of... 3. Fame and prestige - by getting assassinated trying to sell secretly ... would be more than just a paranoid delusion, I would settle for becoming famous. In fact, I hope I've already managed to leave some sort of fingerprint into the history of math thru this: http://tinyurl.com/59u89 Also, with no financial motive, proving that fast factorization is impossible is an equally worthy goal to me, yet way more likely as an outcome. [Furthermore, in case of failing in both, I think I've already managed to leave enough of margin notes in the 'net for the wileses of the future to finish the work :-] Oh well. Then again, what would you bid for the following in eBay? For sale: Sealed envelope. The envelope contains _either_ the complete description of an algorithm for factorizing large integers with such an efficiency, that the time required by the algorithm would increase proportionally to the logarithm of the magnitude of the number being factored, _or_ the proof that no such algorithm can exist. ;-) - Risto - Don't pull a James Harris on us. Please. --- Christopher Heckman If by fast you mean polynomial, then proving that fast factorization is impossible will prove P!=NP. I believe there are financial rewards offered for anyone who can prove P?=NP one way or the other. I personally like your idea. I have always thought that the factoring problem will be solved by seeing it as the problem of reversing the process of multiplication. But what do I know? :-) -- Daniel Jim.8enez djimenez@cs.utexas.edu I've so much music in my head -- Maurice Ravel, shortly before his death. -- John Cage Are you suggesting that the integer factorization problem is known to be NP-complete? If so, this is news to me. Reference? dave You need NP-completeness to push P=NP home, but why do you need it for P!=NP? _Anything_ in NPP proves NP!=P. What matters is that factorisation is in NP. So, if it is not in P, P!=NP. Mike. By the way, a general methos is as follows - suppose that all x,y,z,t are function of one variable, say s. Then will solve corresponding system of ODE - dx/ds=-y+b*z-t dy/ds =(x-z+b*t) dz/ds=-b*x+y-t dt/ds=x-b*y+z let x(s)=f_1(s,C), y(s)=f_2(s,C), z(s)=f_3(s,C), t(s)=f_4(s,C), where C is set of constants C_1,C_2,C_3,C_4. Its well know - every first integral of the system ODE is a solution of yours PDE. To discover those first integrals you have to eliminate variable s from last sysytem. The elimination is an art..I was trying to do it but I have got some complicated expression..but, in any way , you have now a right directions :) Here's a Bessel function... int(cos(z*sin(x)),x=0..Pi)/Pi = J_0(z); For the linear case as that you are dealing with the general solution is straightforward. Here you have a first order difference equation: (1) f(n)-f(n-1)=1 with initial condition: (2) f(0) = 0 You are to find, as with continuous counterpart of differential equation, the general solution to the associated homogeneous equation (f(n)-f(n-1)=0) and a particular solution. Then add them toghether for the general solution. Finally, the solution comes out applying the initial condition. - The general solution of the homogeneous: you have to proceed as with the differential case, that is solving the characteristic equation. While for a first-order linear differential equation the general solution has the following form y = c e^(ax), where a and c are constants, for a firt-order linear difference equation it has the following form (3) f_0(n) = c a^n, where a and c are constants. Substituting (3) in (1) you get an algebraic equation known as the characteristic equation, that you'll solve for a. (4) a = 2 Putting (4) in (3): (5) f_0(n) = c 2^n - The particular solution: with the forcing term being constant the particular solution is constant as well: (6) f_1(n) = k, with k to be determined substituting (6) in (1) Putting (7) in (6): (8) f_1(n) = -1 - The general solution is: (9) f(n) = f_0(n) + f_1(n) Putting (5) and (8) in (9): (10) f(n) = c 2^n - 1 - The solution: making use of (10) and of the initial condition (2) you can obtain k (12) c = 1 Finally, putting (12) in (10) f(n) = 2^n - 1 As you've seen it's very simple, though it's been the simplest case of linear equation and the simplest case of forcing function. Sergio de GIOIA No, for two reasons, both important. (i) the step 5|x-2| < |x-2| is a little fishy (this follows from the fact that 5 < 1, right?) (ii) the step |x-2| = delta is wrong! There's no reason why we should think that |x-2| = delta. Because you left out the words Now if 0 < |x-2| = delta then.... The fact that those words appear in all the proofs you read doesn't mean that they're unimportant so you can leave them out - just the opposite, without those words the proof makes no sense. ************************ David C. Ullrich Content-Length: 7789 Originator: rusin@vesuvius EIGHTEENTH INTERNATIONAL CONFERENCE ON SYSTEMS ENGINEERING (ICSEng05) LAS VEGAS, USA, AUGUST 16-18, 2005 (http://www.icseng.info) This series of International Conferences is jointly organized on a rotational basis among three institutions, University of Nevada, Las Vegas, USA, Technical University of Wroclaw, Poland, and Coventry University, UK. In August 2005, the 18th International Conference will be held in Las Vegas, NV, at the University of Nevada, Las Vegas, USA. The Proceedings of the conference will be published by IEEE CS. 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Hughes College of Engineering University of Nevada, Las Vegas, Las Vegas, NV 89154-4005 Phone: (702) 895 4184 FAX: (702) 895 1115 email: secretary@icseng.info URL: http://www.icseng. info / God forbid. ;-) Really! I stand corrected, then, because I couldn't imagine such a proof and had never heard of the result. Did he prove that no primitives existed like f(x) = a sin (bx + c) for a sufficiently impressive number of functional forms or was he somehow able to represent all finite combinations of elementary functions? I really can't imagine how a proof would go that could handle all such combinations well enough to give a negative result. What framework is it that preserves the class of elementary functions and generates things like sin(ln(x + cos(x)))? *** Integration in Finite Terms *** This theory goes back to Liouville, 1835. Classic text on the subject: J. F. Ritt, _Integration in Finite Terms_ (Columbia Univ Pr, 1948) Introductory papers, aimed at undergraduates: A.D. Fitt & G.T.Q. Hoare, The closed-form integration of arbitrary functions. Mathematical Gazette (1993) 227--236. E. Marchisotto & G. Zakeri, An invitation to integration in finite terms. College Math. J. 25 (1994) 295--308. A modern text (omitting the algebraic case) M. Bronstein, _Symbolic Integration I: Transcendental Functions_ (Springer-Verlag 1997) -- G. A. Edgar http://www.math.ohio-state.edu/~edgar/ It can be done. After all, you're familiar with series, presumably, and could come up with a series for e^(x^2). You could integrate this term by term and find a series that would give you the antiderivative. This is considerably more messy, however, than int(dx/x) = ln(x) + C. This isn't the sort of thing we have to do for most functions. We know how to write int(dx/x) in a nice, concise way because we have the ln function; in fact, some people define ln(x) = int(t = 1 to x, dt/t). You could, if you wanted to, write pq(x) = int(t = 0 to x, exp(t^2) dt). So when someone asked you what the indefinite integral of e^(x^2) was, you'd say pq(x) + C. The difference here is that pq, unlike ln, is not an elementary function. It's not something like a logarithm, sine, cosine, a polynomial, or any other beastie that has been used enough to have a name attached to it. Can't be done? Not at all - don't believe them. Can't be done in closed form (no infinite series) in terms of elementary functions? Seems so, but I don't think anyone has really proven that. Remember first that u = e^x = exp(x) and x = ln u, so you can't treat x like a constant when you are integrating with respect to u. Secondly, are you claiming that int[exp(c*ln(u))du] = (1/c) exp[c*ln(u)] + C? You'll note that exp[c*ln(u)] = u exp c, so int[exp(c*ln(u))du] = int (u exp c du) = (exp c/2) u^2 + C. This is rarely equal to (1/c) exp[c*ln(u)] + C, so something must be going wrong. The formula you are probably half-remembering is int(exp(au)du) = (1/a) exp(au) + C. Unfortunately, you have ln(u) instead of u itself in the exponent, so you can't use this formula. Same thing here; u is a function of x and x is a function of u, so in an integration with respect to one or the other, you can't treat the other as a constant. I'm not sure what's going on here, but it looks like despite writing du you actually took the integral with respect to x. Justin Davis a .8ecrit : A guy called Liouville (heard about him?) did that about 150 years ago. He *really* proved it. I think in fact this should be in the FAQ somewhere. What I meant by really proved it is that after a while some things are taken as loosely proven by time; that seemed to me to be the case as I hadn't heard of the result and couldn't imagine how we might gain a negative result working with such complex (and some seemingly arbitrary(*)) objects. And yes, I've heard of him; G.A. Edgar was kind enough to give references. This seems to be the theorem he proved, for those that are interested: If f and g are rational functions, g not constant, then int(f(x)^g(x)dx) is integrable in finite elementary terms iff there exists a rational function R such that f = R' + Rg. (*) It seems that the class of elementary functions is connected in some way that is unclear to me. I will be looking for the proofs, but I wonder if anyone is already familiar with the idea. The way I thought, it could have been decided a long time ago that pq(x) = int(t = 0 to x, exp(t^2)dt) was an elementary function. Were pq deemed elementary, however, the above theorem wouldn't hold so neatly. What is so special about the elementary functions? Or are we lucky that we picked the elementary functions that we did? Well, of course it can be done at all. e^(x^2) is a continuous function, so it has an antidervative. It's just that the antiderivative can't be expressed as a composition of a fairly arbitrary set of commonly-used functions. Is it possible to give a precis of the proof? I'd guess that Liouville found a form for an arbitrary combination of elementary functions, took its derivative, and extracted from that expression some sort of invariant that differs from that extracted from functions like e^(x^2) ? Is that on the right track? Depends on the value of commonly-used, I suppose. The antiderivative can be written as sqrt(pi/4) erfi(x), where erfi is the imaginary error function. It could also be written in terms of any of an alphabet soup of special functions: parabolic cylinder functions, the exponential integral function Ei, the incomplete Gamma function, Hermite H function, Kummer M and U functions, Laguerre L function, Meijer G function, Whittaker M and W functions, Dawson's function, hypergeometric 1F1 function, etc. Robert Israel israel@math.ubc.ca Department of Mathematics http://www.math.ubc.ca/~israel University of British Columbia Vancouver, BC, Canada This has been posted a number of times. I really really really ought to polish this up for FAQ inclusion. I the general Liouville theory, the second applying this theory to x^x: We give a fairly complete sketch of the proof that certain functions, including the asked for one, are not integrable in elementary terms. The central theorem is due to Liouville in 1835. His proof was analytic. The sketch below is mostly algebraic and is due to Maxwell Rosenlicht. See his papers in the _Pacific Journal of Mathematics_, 54, (1968) 153-161 and 65, (1976), 485-492. WARNING: Prerequisites for understanding the proof is a first year graduate course in algebra, and a little complex analysis. No deep results are used, but I cannot take the time to explain standard notions or all the deductions. Notation: a^n is a power n, a_n is a sub n. C is the complex numbers, for fields F, F[x] is the ring of polynomials in x OR an algebraic extension of F, F(x) is the field of rational functions in a transcendental x, M is the field of meromorphic functions in one variable. If f is a complex function, I(f) will denote an antiderivative of f. A differential field is a field F of characteristic 0 with a derivation. Thus, in addition to the field operations + and *, there is a derivative examples are C(z) and M with the usual derivative map. Notice a basic identity (logarithmic differentiation) holds: [(a_1 ^ k_1) * ... * (a_n ^ k_n)]' a_1' a_n' --------------------------------- = k_1 --- + ... + k_n --- (a_1 ^ k_1) * ... * (a_n ^ k_n) a_1 a_n The usual rules like the quotient rule also hold. If a in F satisfies a'=0, we call a a constant of F. The set of constants of F is called Con(F), and forms a subfield of F. The basic idea in showing something has no elementary integral is to reduce the problem to a sequence of differential fields F_0, F_1, etc., where F_0 = C(z), and F_(i+1) is obtained from F_i by adjoining one new element t. t is obtained either algebraically, because t satisfies some polynomial equation p(t)=0, or exponentially, because t'/t=s' for some s in F_i, or logarithmically, because t'=s'/s is in F_i. Notice that we don't actually take exponentials or logarithms, but only attach abstract elements that have the appropriate derivatives. Thus a function f is integrable in elementary terms iff such a sequence exists starting with C(z). Just so there is no confusion, there is no notion of composition involved here. If you want to take log s, you adjoin a transcendental t with the relation t'=s'/s. There is no log function running around, for example, except as motivation, until we reach actual examples. We need some easy lemmas. Throughout the lemmas F is a differential field, and t is transcendental over F. Lemma 1: If K is an algebraic extension field of F, then there exists a unique way to extend the derivation map from F to K so as to make K into a differential field. Lemma 2: If K=F(t) is a differential field with derivation extending F's, and t' is in F, then for any polynomial f(t) in F[t], f(t)' is a polynomial in F[t] of the same degree (if the leading coefficient is not in Con(F)) or of degree one less (if the leading coefficient is in Con(F)). Lemma 3: If K=F(t) is a differential field with derivation extending F's, and t'/t is in F, then for any a in F, n a positive integer, there exists h in F such that (a*t^n)'=h*t^n. More generally, if f(t) is any polynomial in F[t], then f(t)' is of the same degree as f(t), and is a multiple of f(t) iff f(t) is a monomial. These are all fairly elementary. For example, (a*t^n)'=(a'+at'/t)*t^n in lemma 3. The final 'iff' in lemma 3 is where transcendence of t comes in. Lemma 1 in the usual case of subfields of M can be proven analytically using the implicit function theorem. -------------------------------------------------------------------------- MAIN THEOREM. Let F,G be differential fields, let a be in F, let y be in G, and suppose y'=a and G is an elementary differential extension field of F, and Con(F)=Con(G). Then there exist c_1,...,c_n in Con(F), u_1,...,u_n, v in F such that u_1' u_n' a = c_1 --- + ... + c_n --- + v'. u_1 u_n In other words, the only functions that have elementary anti-derivatives are the ones that have this very specific form. -------------------------------------------------------------------------- This is a very useful theorem for proving non-integrability. In the usual case, F,G are subfields of M, so Con(F)=Con(G)=C always holds. Proof: By assumption there exists a finite chain of fields connecting F to G such that the extension from one field to the next is given by performing an algebraic, logarithmic, or exponential extension. We show that if the form (*) can be satisfied with values in F2, and F2 is one of the three kinds of allowable extensions of F1, then the form (*) can be satisfied in F1. The form (*) is obviously satisfied in G: let all the c's be 0, the u's be 1, and let v be the original y for which y'=a. Thus, if the form (*) can be pulled down one field, we will be able to pull it down to F, and the theorem holds. So we may assume without loss of generality that G=F(t). Case 1: t is algebraic over F. Say t is of degree k. Then there are polynomials U_i and V such that U_i(t)=u_i and V(t)=v. So we have U_1(t)' U_n(t)' a = c_1 ------ + ... + c_n ------ + V(t)'. U_1(t) U_n(t) Now, by the uniqueness of extensions of derivatives in the algebraic case, we may replace t by any of its conjugates t_1,..., t_k, and the same equation holds. In other words, because a is in F, it is fixed under the Galois automorphisms. Summing up over the conjugates, and converting the U'/U terms into products using logarithmic differentiation, we have [U_1(t_1)*...*U_1(t_k)]' k a = c_1 ----------------------- + ... + [V(t_1)+...+V(t_k)]'. U_1(t_1)*...*U_n(t_k) But the expressions in [...] are symmetric polynomials in t_i, and as they are polynomials with coefficients in F, the resulting expressions are in F. So dividing by k gives us (*) holding in F. Case 2: t is logarithmic over F. Because of logarithmic differentiation we may assume that the u's are monic and irreducible in t and distinct. Furthermore, we may assume v has been decomposed into partial fractions. The fractions can only be of the form f/g^j, where deg(f)