Search Results

Search found 329 results on 14 pages for 'differential equations'.

Page 3/14 | < Previous Page | 1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >

  • Large sparse (stiff) ODE system needed for testing

    - by macydanim
    I hope this is the right place for this question. I have been working on a sparse stiff implicit ODE solver and have finished the code so far. I now tested the solver with the Van der Pol equation, and another stiff problem, which is of dimension 4. But to perform better tests I am searching for a bigger system. I'm thinking of the order N = 100...1000, if possible stiff and sparse. Does anybody have an example I could use? I really don't know where to search.

    Read the article

  • How is this number calculated?

    - by Hamid
    I have numbers; A == 0x20000000 B == 18 C == (B/10) D == 0x20000004 == (A + C) A and D are in hex, but I'm not sure what the assumed numeric bases of the others are (although I'd assume base 10 since they don't explicitly state a base. It may or may not be relevant but I'm dealing with memory addresses, A and D are pointers. The part I'm failing to understand is how 18/10 gives me 0x4. Edit: Code for clarity: *address1 (pointer is to address: 0x20000000) printf("Test1: %p\n", address1); printf("Test2: %p\n", address1+(18/10)); printf("Test3: %p\n", address1+(21/10)); Output: Test1: 0x20000000 Test2: 0x20000004 Test3: 0x20000008

    Read the article

  • Equation solver project

    - by Victor Barbu
    I would like to start a project destinated to students. My application has to solve any kind of equation, passed by the user as a string, exactly like in Matlab solve function. How shluld I do this? What programming language is the best for this purpose? Thanks in advance. P.S This is a screenshot made in Matlab. This is how I would like the user to insert and receive the answer: Another example:

    Read the article

  • No more memory available in Mathematica, Fit the parameters of system of differential equation

    - by user1058051
    I encountered a memory problem in Mathematica, when I tried to process my experimental data. It's a system of two differential equations and I need to find most suitable parameters. Unfortunately I am not a Pro in Mathematica, so the program used a lot of memory, when the parameter epsilon is more than 0.4. When it less than 0.4, the program work properly. The command 'historylength = 0' and attempts to reduce the Accuracy Goal and WorkingPrecision didn`t help. I can't use ' clear Cache ', because there isnt a circle. I'm trying to understand what mistakes I made, and how I may limit the memory usage. I have already bought extra-RAM, now its 4GB, and now I haven't free memory-slots in motherboard Remove["Global`*"]; T=13200; L = 0.085; e = 0.41; v = 0.000557197; q = 0.1618; C0 = 0.0256; R = 0.00075; data = {{L,600,0.141124587},{L,1200,0.254134509},{L,1800,0.342888644}, {L,2400,0.424476295},{L,3600,0.562844542},{L,4800,0.657111356}, {L,6000,0.75137817},{L,7200,0.815876516},{L,8430,0.879823594}, {L,9000,0.900771775},{L,13200,1}}; model[(De_)?NumberQ, (Kf_)?NumberQ, (Y_)?NumberQ] := model[De, Kf, Y] = yeld /.Last[Last[ NDSolve[{ v (Ci^(1,0))[z,t]+(Ci^(0,1))[z,t]== -((3 (1-e) Kf (Ci[z,t]-C0))/ (R e (1-(R Kf (1-R/r[z,t]))/De))), (r^(0,1))[z,t]== (R^2 Kf (Ci[z,t]-C0))/ (q r[z,t]^2 (1-(R Kf (1-R/r[z,t]))/De)), (yeld^(0,1))[z,t]== Y*(v e Ci[z,t])/(L q (1-e)), r[z,0]==R, Ci[z,0]==0, Ci[0,t]==0, yeld[z,0]==0}, {r[z,t],Ci[z,t],yeld},{z,0,L},{t,0,T}]]] fit = FindFit[data, {model[De, Kf, Y][z, t], {Y > 0.97, Y < 1.03, Kf > 10^-6, Kf < 10^-4, De > 10^-13, De < 10^-9}}, {{De,7*10^-13}, {Kf, 10^-5}, {Y, 1}}, {z, t}, Method -> NMinimize] data = {{600,0.141124587},{1200,0.254134509},{1800,0.342888644}, {2400,0.424476295},{3600,0.562844542},{4800,0.657111356}, {6000,0.75137817},{7200,0.815876516},{8430,0.879823594}, {9000,0.900771775},{13200,1}}; YYY = model[ De /. fit[[1]], Kf /. fit[[2]], Y /. fit[[3]]]; Show[Plot[Evaluate[YYY[L,t]],{t,0,T},PlotRange->All], ListPlot[data,PlotStyle->Directive[PointSize[Medium],Red]]] the link on the .nb file http://www.4shared.com/folder/249TSjlz/_online.html

    Read the article

  • Difficulty restoring a differential backup in SQL Server, 2 media families are expected or no files are ready for rollforward

    - by digiguru
    I have sql backups copied from server A to server B on a nightly basis. We want to move the sql server from server A to server B without much downtime, but the files are very large. I assumed that performing a differential backup and restore would solve the problem with the databases. Copy full backup from server A to copy to server B (10+gb) Open SQL Server Managment Studio on server B Right mouse on databases Restore Database Type in the new DB-name Choose "From Device" and browse to the backup file Click Okay. This is now resorting the original "full" backup. Test new db with dev application - everything works :) On original database rightmouse on DB Tasks Backup... Backup Type = Differential, Backup to disk, add a new file, and remove the old one (it needs to be a small file to transfer for the smallest amount of outage) Copy the diff backup onto the new db Right mouse on DB Tasks Restore Database This is where I get stuck. If I add both the new differential file, and the original backup to the restore process I get an error The media loaded on "M:\path\to\backup\full.bak" is formatted to support 1 media families, but 2 media families are expected according to the backup device specification. RESTORE HEADERONLY is terminating abnormally. But if I try to restore using just the differential file I get System.Data.SqlClient.SqlError: The log or differential backup cannot be restored because no files are ready to rollforward. (Microsoft.SqlServer.Smo) Any idea how to do it? Is there a better way of restoring backups with limited downtime?

    Read the article

  • Parsing basic math equations for children's educational software?

    - by Simucal
    Inspired by a recent TED talk, I want to write a small piece of educational software. The researcher created little miniature computers in the shape of blocks called "Siftables". [David Merril, inventor - with Siftables in the background.] There were many applications he used the blocks in but my favorite was when each block was a number or basic operation symbol. You could then re-arrange the blocks of numbers or operation symbols in a line, and it would display an answer on another siftable block. So, I've decided I wanted to implemented a software version of "Math Siftables" on a limited scale as my final project for a CS course I'm taking. What is the generally accepted way for parsing and interpreting a string of math expressions, and if they are valid, perform the operation? Is this a case where I should implement a full parser/lexer? I would imagine interpreting basic math expressions would be a semi-common problem in computer science so I'm looking for the right way to approach this. For example, if my Math Siftable blocks where arranged like: [1] [+] [2] This would be a valid sequence and I would perform the necessary operation to arrive at "3". However, if the child were to drag several operation blocks together such as: [2] [\] [\] [5] It would obviously be invalid. Ultimately, I want to be able to parse and interpret any number of chains of operations with the blocks that the user can drag together. Can anyone explain to me or point me to resources for parsing basic math expressions? I'd prefer as much of a language agnostic answer as possible.

    Read the article

  • Finding the intersection of two vector equations.

    - by Matthew Mitchell
    I've been trying to solve this and I found an equation that gives the possibility of zero division errors. Not the best thing: v1 = (a,b) v2 = (c,d) d1 = (e,f) d2 = (h,i) l1: v1 + ?d1 l2: v2 + µd2 Equation to find vector intersection of l1 and l2 programatically by re-arranging for lambda. (a,b) + ?(e,f) = (c,d) + µ(h,i) a + ?e = c + µh b +?f = d + µi µh = a + ?e - c µi = b +?f - d µ = (a + ?e - c)/h µ = (b +?f - d)/i (a + ?e - c)/h = (b +?f - d)/i a/h + ?e/h - c/h = b/i +?f/i - d/i ?e/h - ?f/i = (b/i - d/i) - (a/h - c/h) ?(e/h - f/i) = (b - d)/i - (a - c)/h ? = ((b - d)/i - (a - c)/h)/(e/h - f/i) Intersection vector = (a + ?e,b + ?f) Not sure if it would even work in some cases. I haven't tested it. I need to know how to do this for values as in that example a-i. Thank you.

    Read the article

  • Solving simultaneous equations

    - by Milo
    Here is my problem: Given x, y, z and ratio where z is known and ratio is known and is a float representing a relative value, I need to find x and y. I know that: x / y == ratio y - x == z What I'm trying to do is make my own scroll pane and I'm figuring out the scrollbar parameters. So for example, If the scrollbar must be able to scroll 100 values (z) and the thumb must consume 80% of the bar (ratio = 0.8) then x would be 400 and y would be 500. Thanks

    Read the article

  • Formatting equations in LaTeX

    - by jetsam
    When I include an equation in LaTeX that is enumerated, i.e. {\begin{equation} $$ $$ ... \end{equation} } The line above the equation (blank space between the text preceeding it and the equation) is huge. How do I make it smaller?

    Read the article

  • SQL SERVER – Select the Most Optimal Backup Methods for Server

    - by pinaldave
    Backup and Restore are very interesting concepts and one should be very much with the concept if you are dealing with production database. One never knows when a natural disaster or user error will surface and the first thing everybody wants is to get back on point in time when things were all fine. Well, in this article I have attempted to answer a few of the common questions related to Backup methodology. How to Select a SQL Server Backup Type In order to select a proper SQL Server backup type, a SQL Server administrator needs to understand the difference between the major backup types clearly. Since a picture is worth a thousand words, let me offer it to you below. Select a Recovery Model First The very first question that you should ask yourself is: Can I afford to lose at least a little (15 min, 1 hour, 1 day) worth of data? Resist the temptation to save it all as it comes with the overhead – majority of businesses outside finances can actually afford to lose a bit of data. If your answer is YES, I can afford to lose some data – select a SIMPLE (default) recovery model in the properties of your database, otherwise you need to select a FULL recovery model. The additional advantage of the Full recovery model is that it allows you to restore the data to a specific point in time vs to only last backup time in the Simple recovery model, but it exceeds the scope of this article Backups in SIMPLE Recovery Model In SIMPLE recovery model you can select to do just Full backups or Full + Differential. Full Backup This is the simplest type of backup that contains all information needed to restore the database and should be your first choice. It is often sufficient for small databases, but note that it makes a big impact on the performance of your database Full + Differential Backup After Full, Differential backup picks up all of the changes since the last Full backup. This means if you made Full, Diff, Diff backup – the last Diff backup contains all of the changes and you don’t need the previous Differential backup. Differential backup is obviously smaller and carries less performance overhead Backups in FULL Recovery Model In FULL recovery model you can select Full + Transaction Log or Full + Differential + Transaction Log backup. You have to create Transaction Log backup, because at that time the log is being truncated. Otherwise your Transaction Log will grow uncontrollably. Full + Transaction Log Backup You would always need to perform a Full backup first. Then a series of Transaction log backup. Note that (in contrast to Differential) you need ALL transactions to log since the last Full of Diff backup to properly restore. Transaction log backups have the smallest performance overhead and can be performed often. Full + Differential + Transaction Log Backup If you want to ease the performance overhead on your server, you can replace some of the Full backup in the previous scenario with Differential. You restore scenario would start from Full, then the Last Differential, then all of the remaining transactions log backups Typical backup Scenarios You may say “Well, it is all nice – give me the examples now”. As you may already know, my favorite SQL backup software is SQLBackupAndFTP. If you go to Advanced Backup Schedule form in this program and click “Load a typical backup plan…” link, it will give you these scenarios that I think are quite common – see the image below. The Simplest Way to Schedule SQL Backups I hate to repeat myself, but backup scheduling in SQL agent leaves a lot to be desired. I do not know the simple way to schedule your SQL server backups than in SQLBackupAndFTP – see the image below. The whole backup scheduling with compression, encryption and upload to a Network Folder / HDD / NAS Drive / FTP / Dropbox / Google Drive / Amazon S3 takes just a few minutes – see my previous post for the review. Final Words This post offered an explanation for major backup types only. For more complicated scenarios or to research other options as usually go to MSDN. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: PostADay, SQL, SQL Authority, SQL Backup and Restore, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

    Read the article

  • A Taxonomy of Numerical Methods v1

    - by JoshReuben
    Numerical Analysis – When, What, (but not how) Once you understand the Math & know C++, Numerical Methods are basically blocks of iterative & conditional math code. I found the real trick was seeing the forest for the trees – knowing which method to use for which situation. Its pretty easy to get lost in the details – so I’ve tried to organize these methods in a way that I can quickly look this up. I’ve included links to detailed explanations and to C++ code examples. I’ve tried to classify Numerical methods in the following broad categories: Solving Systems of Linear Equations Solving Non-Linear Equations Iteratively Interpolation Curve Fitting Optimization Numerical Differentiation & Integration Solving ODEs Boundary Problems Solving EigenValue problems Enjoy – I did ! Solving Systems of Linear Equations Overview Solve sets of algebraic equations with x unknowns The set is commonly in matrix form Gauss-Jordan Elimination http://en.wikipedia.org/wiki/Gauss%E2%80%93Jordan_elimination C++: http://www.codekeep.net/snippets/623f1923-e03c-4636-8c92-c9dc7aa0d3c0.aspx Produces solution of the equations & the coefficient matrix Efficient, stable 2 steps: · Forward Elimination – matrix decomposition: reduce set to triangular form (0s below the diagonal) or row echelon form. If degenerate, then there is no solution · Backward Elimination –write the original matrix as the product of ints inverse matrix & its reduced row-echelon matrix à reduce set to row canonical form & use back-substitution to find the solution to the set Elementary ops for matrix decomposition: · Row multiplication · Row switching · Add multiples of rows to other rows Use pivoting to ensure rows are ordered for achieving triangular form LU Decomposition http://en.wikipedia.org/wiki/LU_decomposition C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-lu-decomposition-for-solving.html Represent the matrix as a product of lower & upper triangular matrices A modified version of GJ Elimination Advantage – can easily apply forward & backward elimination to solve triangular matrices Techniques: · Doolittle Method – sets the L matrix diagonal to unity · Crout Method - sets the U matrix diagonal to unity Note: both the L & U matrices share the same unity diagonal & can be stored compactly in the same matrix Gauss-Seidel Iteration http://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method C++: http://www.nr.com/forum/showthread.php?t=722 Transform the linear set of equations into a single equation & then use numerical integration (as integration formulas have Sums, it is implemented iteratively). an optimization of Gauss-Jacobi: 1.5 times faster, requires 0.25 iterations to achieve the same tolerance Solving Non-Linear Equations Iteratively find roots of polynomials – there may be 0, 1 or n solutions for an n order polynomial use iterative techniques Iterative methods · used when there are no known analytical techniques · Requires set functions to be continuous & differentiable · Requires an initial seed value – choice is critical to convergence à conduct multiple runs with different starting points & then select best result · Systematic - iterate until diminishing returns, tolerance or max iteration conditions are met · bracketing techniques will always yield convergent solutions, non-bracketing methods may fail to converge Incremental method if a nonlinear function has opposite signs at 2 ends of a small interval x1 & x2, then there is likely to be a solution in their interval – solutions are detected by evaluating a function over interval steps, for a change in sign, adjusting the step size dynamically. Limitations – can miss closely spaced solutions in large intervals, cannot detect degenerate (coinciding) solutions, limited to functions that cross the x-axis, gives false positives for singularities Fixed point method http://en.wikipedia.org/wiki/Fixed-point_iteration C++: http://books.google.co.il/books?id=weYj75E_t6MC&pg=PA79&lpg=PA79&dq=fixed+point+method++c%2B%2B&source=bl&ots=LQ-5P_taoC&sig=lENUUIYBK53tZtTwNfHLy5PEWDk&hl=en&sa=X&ei=wezDUPW1J5DptQaMsIHQCw&redir_esc=y#v=onepage&q=fixed%20point%20method%20%20c%2B%2B&f=false Algebraically rearrange a solution to isolate a variable then apply incremental method Bisection method http://en.wikipedia.org/wiki/Bisection_method C++: http://numericalcomputing.wordpress.com/category/algorithms/ Bracketed - Select an initial interval, keep bisecting it ad midpoint into sub-intervals and then apply incremental method on smaller & smaller intervals – zoom in Adv: unaffected by function gradient à reliable Disadv: slow convergence False Position Method http://en.wikipedia.org/wiki/False_position_method C++: http://www.dreamincode.net/forums/topic/126100-bisection-and-false-position-methods/ Bracketed - Select an initial interval , & use the relative value of function at interval end points to select next sub-intervals (estimate how far between the end points the solution might be & subdivide based on this) Newton-Raphson method http://en.wikipedia.org/wiki/Newton's_method C++: http://www-users.cselabs.umn.edu/classes/Summer-2012/csci1113/index.php?page=./newt3 Also known as Newton's method Convenient, efficient Not bracketed – only a single initial guess is required to start iteration – requires an analytical expression for the first derivative of the function as input. Evaluates the function & its derivative at each step. Can be extended to the Newton MutiRoot method for solving multiple roots Can be easily applied to an of n-coupled set of non-linear equations – conduct a Taylor Series expansion of a function, dropping terms of order n, rewrite as a Jacobian matrix of PDs & convert to simultaneous linear equations !!! Secant Method http://en.wikipedia.org/wiki/Secant_method C++: http://forum.vcoderz.com/showthread.php?p=205230 Unlike N-R, can estimate first derivative from an initial interval (does not require root to be bracketed) instead of inputting it Since derivative is approximated, may converge slower. Is fast in practice as it does not have to evaluate the derivative at each step. Similar implementation to False Positive method Birge-Vieta Method http://mat.iitm.ac.in/home/sryedida/public_html/caimna/transcendental/polynomial%20methods/bv%20method.html C++: http://books.google.co.il/books?id=cL1boM2uyQwC&pg=SA3-PA51&lpg=SA3-PA51&dq=Birge-Vieta+Method+c%2B%2B&source=bl&ots=QZmnDTK3rC&sig=BPNcHHbpR_DKVoZXrLi4nVXD-gg&hl=en&sa=X&ei=R-_DUK2iNIjzsgbE5ID4Dg&redir_esc=y#v=onepage&q=Birge-Vieta%20Method%20c%2B%2B&f=false combines Horner's method of polynomial evaluation (transforming into lesser degree polynomials that are more computationally efficient to process) with Newton-Raphson to provide a computational speed-up Interpolation Overview Construct new data points for as close as possible fit within range of a discrete set of known points (that were obtained via sampling, experimentation) Use Taylor Series Expansion of a function f(x) around a specific value for x Linear Interpolation http://en.wikipedia.org/wiki/Linear_interpolation C++: http://www.hamaluik.com/?p=289 Straight line between 2 points à concatenate interpolants between each pair of data points Bilinear Interpolation http://en.wikipedia.org/wiki/Bilinear_interpolation C++: http://supercomputingblog.com/graphics/coding-bilinear-interpolation/2/ Extension of the linear function for interpolating functions of 2 variables – perform linear interpolation first in 1 direction, then in another. Used in image processing – e.g. texture mapping filter. Uses 4 vertices to interpolate a value within a unit cell. Lagrange Interpolation http://en.wikipedia.org/wiki/Lagrange_polynomial C++: http://www.codecogs.com/code/maths/approximation/interpolation/lagrange.php For polynomials Requires recomputation for all terms for each distinct x value – can only be applied for small number of nodes Numerically unstable Barycentric Interpolation http://epubs.siam.org/doi/pdf/10.1137/S0036144502417715 C++: http://www.gamedev.net/topic/621445-barycentric-coordinates-c-code-check/ Rearrange the terms in the equation of the Legrange interpolation by defining weight functions that are independent of the interpolated value of x Newton Divided Difference Interpolation http://en.wikipedia.org/wiki/Newton_polynomial C++: http://jee-appy.blogspot.co.il/2011/12/newton-divided-difference-interpolation.html Hermite Divided Differences: Interpolation polynomial approximation for a given set of data points in the NR form - divided differences are used to approximately calculate the various differences. For a given set of 3 data points , fit a quadratic interpolant through the data Bracketed functions allow Newton divided differences to be calculated recursively Difference table Cubic Spline Interpolation http://en.wikipedia.org/wiki/Spline_interpolation C++: https://www.marcusbannerman.co.uk/index.php/home/latestarticles/42-articles/96-cubic-spline-class.html Spline is a piecewise polynomial Provides smoothness – for interpolations with significantly varying data Use weighted coefficients to bend the function to be smooth & its 1st & 2nd derivatives are continuous through the edge points in the interval Curve Fitting A generalization of interpolating whereby given data points may contain noise à the curve does not necessarily pass through all the points Least Squares Fit http://en.wikipedia.org/wiki/Least_squares C++: http://www.ccas.ru/mmes/educat/lab04k/02/least-squares.c Residual – difference between observed value & expected value Model function is often chosen as a linear combination of the specified functions Determines: A) The model instance in which the sum of squared residuals has the least value B) param values for which model best fits data Straight Line Fit Linear correlation between independent variable and dependent variable Linear Regression http://en.wikipedia.org/wiki/Linear_regression C++: http://www.oocities.org/david_swaim/cpp/linregc.htm Special case of statistically exact extrapolation Leverage least squares Given a basis function, the sum of the residuals is determined and the corresponding gradient equation is expressed as a set of normal linear equations in matrix form that can be solved (e.g. using LU Decomposition) Can be weighted - Drop the assumption that all errors have the same significance –-> confidence of accuracy is different for each data point. Fit the function closer to points with higher weights Polynomial Fit - use a polynomial basis function Moving Average http://en.wikipedia.org/wiki/Moving_average C++: http://www.codeproject.com/Articles/17860/A-Simple-Moving-Average-Algorithm Used for smoothing (cancel fluctuations to highlight longer-term trends & cycles), time series data analysis, signal processing filters Replace each data point with average of neighbors. Can be simple (SMA), weighted (WMA), exponential (EMA). Lags behind latest data points – extra weight can be given to more recent data points. Weights can decrease arithmetically or exponentially according to distance from point. Parameters: smoothing factor, period, weight basis Optimization Overview Given function with multiple variables, find Min (or max by minimizing –f(x)) Iterative approach Efficient, but not necessarily reliable Conditions: noisy data, constraints, non-linear models Detection via sign of first derivative - Derivative of saddle points will be 0 Local minima Bisection method Similar method for finding a root for a non-linear equation Start with an interval that contains a minimum Golden Search method http://en.wikipedia.org/wiki/Golden_section_search C++: http://www.codecogs.com/code/maths/optimization/golden.php Bisect intervals according to golden ratio 0.618.. Achieves reduction by evaluating a single function instead of 2 Newton-Raphson Method Brent method http://en.wikipedia.org/wiki/Brent's_method C++: http://people.sc.fsu.edu/~jburkardt/cpp_src/brent/brent.cpp Based on quadratic or parabolic interpolation – if the function is smooth & parabolic near to the minimum, then a parabola fitted through any 3 points should approximate the minima – fails when the 3 points are collinear , in which case the denominator is 0 Simplex Method http://en.wikipedia.org/wiki/Simplex_algorithm C++: http://www.codeguru.com/cpp/article.php/c17505/Simplex-Optimization-Algorithm-and-Implemetation-in-C-Programming.htm Find the global minima of any multi-variable function Direct search – no derivatives required At each step it maintains a non-degenerative simplex – a convex hull of n+1 vertices. Obtains the minimum for a function with n variables by evaluating the function at n-1 points, iteratively replacing the point of worst result with the point of best result, shrinking the multidimensional simplex around the best point. Point replacement involves expanding & contracting the simplex near the worst value point to determine a better replacement point Oscillation can be avoided by choosing the 2nd worst result Restart if it gets stuck Parameters: contraction & expansion factors Simulated Annealing http://en.wikipedia.org/wiki/Simulated_annealing C++: http://code.google.com/p/cppsimulatedannealing/ Analogy to heating & cooling metal to strengthen its structure Stochastic method – apply random permutation search for global minima - Avoid entrapment in local minima via hill climbing Heating schedule - Annealing schedule params: temperature, iterations at each temp, temperature delta Cooling schedule – can be linear, step-wise or exponential Differential Evolution http://en.wikipedia.org/wiki/Differential_evolution C++: http://www.amichel.com/de/doc/html/ More advanced stochastic methods analogous to biological processes: Genetic algorithms, evolution strategies Parallel direct search method against multiple discrete or continuous variables Initial population of variable vectors chosen randomly – if weighted difference vector of 2 vectors yields a lower objective function value then it replaces the comparison vector Many params: #parents, #variables, step size, crossover constant etc Convergence is slow – many more function evaluations than simulated annealing Numerical Differentiation Overview 2 approaches to finite difference methods: · A) approximate function via polynomial interpolation then differentiate · B) Taylor series approximation – additionally provides error estimate Finite Difference methods http://en.wikipedia.org/wiki/Finite_difference_method C++: http://www.wpi.edu/Pubs/ETD/Available/etd-051807-164436/unrestricted/EAMPADU.pdf Find differences between high order derivative values - Approximate differential equations by finite differences at evenly spaced data points Based on forward & backward Taylor series expansion of f(x) about x plus or minus multiples of delta h. Forward / backward difference - the sums of the series contains even derivatives and the difference of the series contains odd derivatives – coupled equations that can be solved. Provide an approximation of the derivative within a O(h^2) accuracy There is also central difference & extended central difference which has a O(h^4) accuracy Richardson Extrapolation http://en.wikipedia.org/wiki/Richardson_extrapolation C++: http://mathscoding.blogspot.co.il/2012/02/introduction-richardson-extrapolation.html A sequence acceleration method applied to finite differences Fast convergence, high accuracy O(h^4) Derivatives via Interpolation Cannot apply Finite Difference method to discrete data points at uneven intervals – so need to approximate the derivative of f(x) using the derivative of the interpolant via 3 point Lagrange Interpolation Note: the higher the order of the derivative, the lower the approximation precision Numerical Integration Estimate finite & infinite integrals of functions More accurate procedure than numerical differentiation Use when it is not possible to obtain an integral of a function analytically or when the function is not given, only the data points are Newton Cotes Methods http://en.wikipedia.org/wiki/Newton%E2%80%93Cotes_formulas C++: http://www.siafoo.net/snippet/324 For equally spaced data points Computationally easy – based on local interpolation of n rectangular strip areas that is piecewise fitted to a polynomial to get the sum total area Evaluate the integrand at n+1 evenly spaced points – approximate definite integral by Sum Weights are derived from Lagrange Basis polynomials Leverage Trapezoidal Rule for default 2nd formulas, Simpson 1/3 Rule for substituting 3 point formulas, Simpson 3/8 Rule for 4 point formulas. For 4 point formulas use Bodes Rule. Higher orders obtain more accurate results Trapezoidal Rule uses simple area, Simpsons Rule replaces the integrand f(x) with a quadratic polynomial p(x) that uses the same values as f(x) for its end points, but adds a midpoint Romberg Integration http://en.wikipedia.org/wiki/Romberg's_method C++: http://code.google.com/p/romberg-integration/downloads/detail?name=romberg.cpp&can=2&q= Combines trapezoidal rule with Richardson Extrapolation Evaluates the integrand at equally spaced points The integrand must have continuous derivatives Each R(n,m) extrapolation uses a higher order integrand polynomial replacement rule (zeroth starts with trapezoidal) à a lower triangular matrix set of equation coefficients where the bottom right term has the most accurate approximation. The process continues until the difference between 2 successive diagonal terms becomes sufficiently small. Gaussian Quadrature http://en.wikipedia.org/wiki/Gaussian_quadrature C++: http://www.alglib.net/integration/gaussianquadratures.php Data points are chosen to yield best possible accuracy – requires fewer evaluations Ability to handle singularities, functions that are difficult to evaluate The integrand can include a weighting function determined by a set of orthogonal polynomials. Points & weights are selected so that the integrand yields the exact integral if f(x) is a polynomial of degree <= 2n+1 Techniques (basically different weighting functions): · Gauss-Legendre Integration w(x)=1 · Gauss-Laguerre Integration w(x)=e^-x · Gauss-Hermite Integration w(x)=e^-x^2 · Gauss-Chebyshev Integration w(x)= 1 / Sqrt(1-x^2) Solving ODEs Use when high order differential equations cannot be solved analytically Evaluated under boundary conditions RK for systems – a high order differential equation can always be transformed into a coupled first order system of equations Euler method http://en.wikipedia.org/wiki/Euler_method C++: http://rosettacode.org/wiki/Euler_method First order Runge–Kutta method. Simple recursive method – given an initial value, calculate derivative deltas. Unstable & not very accurate (O(h) error) – not used in practice A first-order method - the local error (truncation error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step size In evolving solution between data points xn & xn+1, only evaluates derivatives at beginning of interval xn à asymmetric at boundaries Higher order Runge Kutta http://en.wikipedia.org/wiki/Runge%E2%80%93Kutta_methods C++: http://www.dreamincode.net/code/snippet1441.htm 2nd & 4th order RK - Introduces parameterized midpoints for more symmetric solutions à accuracy at higher computational cost Adaptive RK – RK-Fehlberg – estimate the truncation at each integration step & automatically adjust the step size to keep error within prescribed limits. At each step 2 approximations are compared – if in disagreement to a specific accuracy, the step size is reduced Boundary Value Problems Where solution of differential equations are located at 2 different values of the independent variable x à more difficult, because cannot just start at point of initial value – there may not be enough starting conditions available at the end points to produce a unique solution An n-order equation will require n boundary conditions – need to determine the missing n-1 conditions which cause the given conditions at the other boundary to be satisfied Shooting Method http://en.wikipedia.org/wiki/Shooting_method C++: http://ganeshtiwaridotcomdotnp.blogspot.co.il/2009/12/c-c-code-shooting-method-for-solving.html Iteratively guess the missing values for one end & integrate, then inspect the discrepancy with the boundary values of the other end to adjust the estimate Given the starting boundary values u1 & u2 which contain the root u, solve u given the false position method (solving the differential equation as an initial value problem via 4th order RK), then use u to solve the differential equations. Finite Difference Method For linear & non-linear systems Higher order derivatives require more computational steps – some combinations for boundary conditions may not work though Improve the accuracy by increasing the number of mesh points Solving EigenValue Problems An eigenvalue can substitute a matrix when doing matrix multiplication à convert matrix multiplication into a polynomial EigenValue For a given set of equations in matrix form, determine what are the solution eigenvalue & eigenvectors Similar Matrices - have same eigenvalues. Use orthogonal similarity transforms to reduce a matrix to diagonal form from which eigenvalue(s) & eigenvectors can be computed iteratively Jacobi method http://en.wikipedia.org/wiki/Jacobi_method C++: http://people.sc.fsu.edu/~jburkardt/classes/acs2_2008/openmp/jacobi/jacobi.html Robust but Computationally intense – use for small matrices < 10x10 Power Iteration http://en.wikipedia.org/wiki/Power_iteration For any given real symmetric matrix, generate the largest single eigenvalue & its eigenvectors Simplest method – does not compute matrix decomposition à suitable for large, sparse matrices Inverse Iteration Variation of power iteration method – generates the smallest eigenvalue from the inverse matrix Rayleigh Method http://en.wikipedia.org/wiki/Rayleigh's_method_of_dimensional_analysis Variation of power iteration method Rayleigh Quotient Method Variation of inverse iteration method Matrix Tri-diagonalization Method Use householder algorithm to reduce an NxN symmetric matrix to a tridiagonal real symmetric matrix vua N-2 orthogonal transforms     Whats Next Outside of Numerical Methods there are lots of different types of algorithms that I’ve learned over the decades: Data Mining – (I covered this briefly in a previous post: http://geekswithblogs.net/JoshReuben/archive/2007/12/31/ssas-dm-algorithms.aspx ) Search & Sort Routing Problem Solving Logical Theorem Proving Planning Probabilistic Reasoning Machine Learning Solvers (eg MIP) Bioinformatics (Sequence Alignment, Protein Folding) Quant Finance (I read Wilmott’s books – interesting) Sooner or later, I’ll cover the above topics as well.

    Read the article

  • Why can't flowcharts or mathematical equations created in Microsoft Office and saved in .docx format be opened by LibreOffice?

    - by user33831
    I am using Ubuntu 11.10 and LibreOffice that comes with it. Before this , I was a Windows user and some of my previous documents were saved in .docx format. I tried to use LibreOffice to open those .docx file and I can view all text, however I can't view the flowchart I drew and also mathematical equations. Another issue is, if I create new flowchart with LibreOffice and save it in .docx file, when I re-open that file, I can't view those flowcharts, but those flowcharts are there, occupied space. No problem for .odt format of course. Does anyone know why this happens? Thanks in advanced.

    Read the article

  • Win 7 Home Premium 64 bit running Cobian Backup 11 (Gravity)

    - by Andrew
    I'm really enjoying Cobian 11, but am fairly new to it. My question is this. I back up a pretty large folder on a regular basis. I started off by doing a Full backup, and have followed that monthly using differential backups. I was told that, to restore my computer after a crash, I need to copy back the original full backup AND copy back the latest differential over the full. That's fine. However, over the months there are quite a few large differential backups dated between the original Full one and the latest differential one. To free space on my backup HD, can I every now and then delete the differential backups that lie between the original Full and the latest differential, and just leave the original Full and the latest differential backup on the HD?

    Read the article

  • Runge-Kutta Method with adaptive step

    - by infoholic_anonymous
    I am implementing Runge-Kutta method with adaptive step in matlab. I get different results as compared to matlab's own ode45 and my own implementation of Runge-Kutta method with fixed step. What am I doing wrong in my code? Is it possible? function [ result ] = rk4_modh( f, int, init, h, h_min ) % % f - function handle % int - interval - pair (x_min, x_max) % init - initial conditions - pair (y1(0),y2(0)) % h_min - lower limit for h (step length) % h - initial step length % x - independent variable ( for example time ) % y - dependent variable - vertical vector - in our case ( y1, y2 ) function [ k1, k2, k3, k4, ka, y ] = iteration( f, h, x, y ) % core functionality performed within loop k1 = h * f(x,y); k2 = h * f(x+h/2, y+k1/2); k3 = h * f(x+h/2, y+k2/2); k4 = h * f(x+h, y+k3); ka = (k1 + 2*k2 + 2*k3 + k4)/6; y = y + ka; end % constants % relative error eW = 1e-10; % absolute error eB = 1e-10; s = 0.9; b = 5; % initialization i = 1; x = int(1); y = init; while true hy = y; hx = x; %algorithm [ k1, k2, k3, k4, ka, y ] = iteration( f, h, x, y ); % error estimation for j=1:2 [ hk1, hk2, hk3, hk4, hka, hy ] = iteration( f, h/2, hx, hy ); hx = hx + h/2; end err(:,i) = abs(hy - y); % step adjustment e = abs( hy ) * eW + eB; a = min( e ./ err(:,i) )^(0.2); mul = a * s; if mul >= 1 % step length admitted keepH(i) = h; k(:,:,i) = [ k1, k2, k3, k4, ka ]; previous(i,:) = [ x+h, y' ]; %' i = i + 1; if floor( x + h + eB ) == int(2) break; else h = min( [mul*h, b*h, int(2)-x] ); x = x + keepH(i-1); end else % step length requires further adjustments h = mul * h; if ( h < h_min ) error('Computation with given precision impossible'); end end end result = struct( 'val', previous, 'k', k, 'err', err, 'h', keepH ); end The function in question is: function [ res ] = fun( x, y ) % res(1) = y(2) + y(1) * ( 0.9 - y(1)^2 - y(2)^2 ); res(2) = -y(1) + y(2) * ( 0.9 - y(1)^2 - y(2)^2 ); res = res'; %' end The call is: res = rk4( @fun, [0,20], [0.001; 0.001], 0.008 ); The resulting plot for x1 : The result of ode45( @fun, [0, 20], [0.001, 0.001] ) is:

    Read the article

  • Latex --- Is there a way to shift the equation numbering one tab space from the right margin (shift

    - by Murari
    I have been formatting my dissertation and one little problem is stucking me up. I used the following code to typeset an equation \begin{align} & R=\frac{P^2}{P+S'} \label{eqn:SCS}\\ &\mbox {where} \quad \mbox R = \mbox {Watershed Runoff} \notag\\ &\hspace{0.63in} \mbox P = \mbox{Rainfall} \notag\\ &\hspace{0.63in} \mbox S' = \mbox{Storage in the watershed $=\frac{1000}{CN}-10$ }\notag \end{align} My output requirement is such that: The equation should begin one tab space from the left margin The equation number should end at one tab space from the right margin With the above code, I have the equation begin at the right place but not the numbering. Any help will be extremely appreciated. Thanks MP

    Read the article

  • What's the fastest way to approximate the period of data using Octave?

    - by John
    I have a set of data that is periodic (but not sinusoidal). I have a set of time values in one vector and a set of amplitudes in a second vector. I'd like to quickly approximate the period of the function. Any suggestions? Specifically, here's my current code. I'd like to approximate the period of the vector x(:,2) against the vector t. Ultimately, I'd like to do this for lots of initial conditions and calculate the period of each and plot the result. function xdot = f (x,t) xdot(1) =x(2); xdot(2) =-sin(x(1)); endfunction x0=[1;1.75]; #eventually, I'd like to try lots of values for x0(2) t = linspace (0, 50, 200); x = lsode ("f", x0, t) plot(x(:,1),x(:,2)); Thank you! John

    Read the article

  • Is there a way to shift the equation numbering one tab space from the right margin (shift towards le

    - by Murari
    I have been formatting my dissertation and one little problem is stucking me up. I used the following code to typeset an equation \begin{align} & R=\frac{P^2}{P+S'} \label{eqn:SCS}\\ &\mbox {where} \quad \mbox R = \mbox {Watershed Runoff} \notag\\ &\hspace{0.63in} \mbox P = \mbox{Rainfall} \notag\\ &\hspace{0.63in} \mbox S' = \mbox{Storage in the watershed $=\frac{1000}{CN}-10$ }\notag \end{align} My output requirement is such that: The equation should begin one tab space from the left margin The equation number should end at one tab space from the right margin With the above code, I have the equation begin at the right place but not the numbering. Any help will be extremely appreciated. Thanks MP

    Read the article

  • How to determine if you should use full or differential backup?

    - by Peter Larsson
    Or ask yourself, "How much of the database has changed since last backup?". Here is a simple script that will tell you how much (in percent) have changed in the database since last backup. -- Prepare staging table for all DBCC outputs DECLARE @Sample TABLE         (             Col1 VARCHAR(MAX) NOT NULL,             Col2 VARCHAR(MAX) NOT NULL,             Col3 VARCHAR(MAX) NOT NULL,             Col4 VARCHAR(MAX) NOT NULL,             Col5 VARCHAR(MAX)         )   -- Some intermediate variables for controlling loop DECLARE @FileNum BIGINT = 1,         @PageNum BIGINT = 6,         @SQL VARCHAR(100),         @Error INT,         @DatabaseName SYSNAME = 'Yoda'   -- Loop all files to the very end WHILE 1 = 1     BEGIN         BEGIN TRY             -- Build the SQL string to execute             SET     @SQL = 'DBCC PAGE(' + QUOTENAME(@DatabaseName) + ', ' + CAST(@FileNum AS VARCHAR(50)) + ', '                             + CAST(@PageNum AS VARCHAR(50)) + ', 3) WITH TABLERESULTS'               -- Insert the DBCC output in the staging table             INSERT  @Sample                     (                         Col1,                         Col2,                         Col3,                         Col4                     )             EXEC    (@SQL)               -- DCM pages exists at an interval             SET    @PageNum += 511232         END TRY           BEGIN CATCH             -- If error and first DCM page does not exist, all files are read             IF @PageNum = 6                 BREAK             ELSE                 -- If no more DCM, increase filenum and start over                 SELECT  @FileNum += 1,                         @PageNum = 6         END CATCH     END   -- Delete all records not related to diff information DELETE FROM    @Sample WHERE   Col1 NOT LIKE 'DIFF%'   -- Split the range UPDATE  @Sample SET     Col5 = PARSENAME(REPLACE(Col3, ' - ', '.'), 1),         Col3 = PARSENAME(REPLACE(Col3, ' - ', '.'), 2)   -- Remove last paranthesis UPDATE  @Sample SET     Col3 = RTRIM(REPLACE(Col3, ')', '')),         Col5 = RTRIM(REPLACE(Col5, ')', ''))   -- Remove initial information about filenum UPDATE  @Sample SET     Col3 = SUBSTRING(Col3, CHARINDEX(':', Col3) + 1, 8000),         Col5 = SUBSTRING(Col5, CHARINDEX(':', Col5) + 1, 8000)   -- Prepare data outtake ;WITH cteSource(Changed, [PageCount]) AS (     SELECT      Changed,                 SUM(COALESCE(ToPage, FromPage) - FromPage + 1) AS [PageCount]     FROM        (                     SELECT CAST(Col3 AS INT) AS FromPage,                             CAST(NULLIF(Col5, '') AS INT) AS ToPage,                             LTRIM(Col4) AS Changed                     FROM    @Sample                 ) AS d     GROUP BY    Changed     WITH ROLLUP ) -- Present the final result SELECT  COALESCE(Changed, 'TOTAL PAGES') AS Changed,         [PageCount],         100.E * [PageCount] / SUM(CASE WHEN Changed IS NULL THEN 0 ELSE [PageCount] END) OVER () AS Percentage FROM    cteSource

    Read the article

  • How to type all the math, stat, greek, equations EFFICIENTLY in libreoffice?

    - by kernel_panic
    i am preparing a report related to physics which is full of greek, stat and calculus things, i know there is this question how to insert a greek symbol, but my problem is i cant fiddle with a drop down/ scroll list for for every symbol(my paper in FULL of those), is there a way to do something with my keyboard layout, and turn it into something like the one Tony Stark uses in Ironman(i am not kidding please). i am literally tired for this fiddle-work for half of the day and have completed just 2 sheets, hmmm.

    Read the article

  • Force apt-get to ask all install equations again?

    - by user204744
    I made a mistake. I answered the wrong way when installing something with apt-get. Now, when I purge it and reinstall it, it doesn't ask me the question any more. How do I force apt-get to ask me the question again? I'd really rather do this through the package manager than making the changes manually; that's the whole point of using a package manager.... The package in question is jackd. The first time I installed it, I answered "no" to the question about real-time priority. Now, I wish I'd said yes. However, even though I purge and install it again, I don't get the question. (I have tried dpkg-reconfigure jackd -- that doesn't give me any questions to answer.)

    Read the article

  • Are non-modified FILESTREAM files excluded from DIFFERENTIAL backups?

    - by TiborKaraszi
    Short answer seems to be "yes". I got this from a forum post today, so I thought I'd test it out. Basically, the discussion is whether we can somehow cut down backup sizes for filestream data (assumption is that filestream data isn't modified very frequently). I've seen this a few times now, and often suggestions arises to somehow exclude the filestream data and fo file level backup of those files. But that would more or less leaves us with the problem of the "old" solution: potential inconsistency....(read more)

    Read the article

< Previous Page | 1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >