0. (Multiply the linear factors together, but leave P(x) as a sum of 3 quadratics in the variable x.) DELIVERABLES: All your work in constructing the polynomial. b) Derive the quadrature formula of
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Q1
a)Determine the Lagrange form of the interpolating polynomial P(x) that interpolates a function f(x) at x = 0, h and 3h, where h > 0. (Multiply the linear factors together, but leave P(x) as a sum of 3 quadratics in the variable x.) DELIVERABLES: All your work in constructing the polynomial.
b) Derive the quadrature formula of the form a0f(0) + a1f(h) + a2f(3h) for approximating I = R 3h 0 f(x)dx that results from approximating the integral I by I ≈ R 3h 0 P(x)dx.
Note: if you know only 3 function values of f(x) and they are at 3 unequally-spaced points 0, h and 3h, then this kind of quadrature formula can be used to approximate I. DELIVERABLES: All your work in deriving the quadrature formula.
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- 5. (a) Write a function that solves the general linear least-squares problem. The inputs to your function should be a vector of a values, a vector of measured y values, and an anonymous function that calculates a single row of the Z matrix. Inside your function, use the inputted anonymous function to create Z row-by-row, then use Z to create the normal equations. Solve these normal equations to obtain the computed coefficients that define the best-fit of your model. Your function should output these calculated coefficients. Again, include an error check that makes sure the input vectors are the same size. You may not use any built-in MATLAB functions to solve the normal equation. You can use any functions you have developed solves Ax = b via LU-decomposition is one option. e.g. a function you have that (b) Test your function in (a) to fit the following model to the given dataset (see belo y = a + bx (c) Test your function in (a) to fit the following model to the given dataset…I'm having trouble understanding what exactly makes a linear ordinary differential equation "linear". I know that the major advantage of the linear ODEs is that a linear combination of particular solutions gives another particular solution. Characterized by additivity and homogeneity, this means that the output for a sum of inputs is equal to the sum of outputs for each individual input (e.g., f(3) + f(5) = f(3+5)= f(8)) and scaling the input by a factor scales the output by the same factor (e.g., f(6x) =6f(x)...but how would this look? Like say if I had 4e^5(t-1) satisfying the differential equation dy/dt=5y where y(1)=4, what would that "linear combination" look like?et �=[5914], �=[−5−3−5], and �=[558]Are �,� and � linearly dependent, or are they linearly independent?Linearly independentLinearly dependentIf they are linearly dependent, determine a non-trivial linear relation. Otherwise, if the vectors are linearly independent, enter 0's for the coefficients, since that relationship always holds.�+ �+ �=0.
- 2. Heat conduction in a square plate Three sides of a rectangular plate (@ = 5 m, b = 4 m) are kept at a temperature of 0 C and one side is kept at a temperature C, as shown in the figure. Determine and plot the ; temperature distribution T(x, y) in the plate. The temperature distribution, T(x, y) in the plate can be determined by solving the two-dimensional heat equation. For the given boundary conditions T(x, y) can be expressed analytically by a Fourier series (Erwin Kreyszig, Advanced Engineering Mathematics, John Wiley and Sons, 1993):1. Develop a system of linear equations for the network by writing an equation for each router (A, B, C, D, and E). Make sure to write your final answer as Ax=b where A is the 5x5 coefficient matrix, x is the 5x1 vector of unknowns, and b is a 5x1 vector of constants. 2. construct the augmented matrix [A b] and then perform row reduction using the rref() function. Write out your reduced matrix and identify the free and basic variables of the system. 3. Use MATLAB to compute the LU decomposition of A, i.e., find A = LU. For this decomposition, find the transformed set of equations Ly = b, where y = Ux. Solve the system of equations Ly = b for the unknown vector y. 4. Compute the inverse of U using the inv() function. 5. Compute the solution to the original system of equations by transforming y into x, i.e., compute x = inv(U)y 6. Check your answer for x1 using Cramer’s Rule. Use MATLAB to compute the required determinants using the det() function.Answer the following problems;--
- Electromagnetic Pulse propagating at oblique angle to a dielectric interface Consider a gaussian wave pulse propagating along the z-axis from region 1 with refractive index n1 and onto a dielectric interface y = m z (for all x). To the left of this dielectric interface, the refractive index is n2. Devise an initial value computer algorithm to determine the time evolution of the reflected and transmitted electromagnetic fields for this pulse. e.g., n1 = 1 , n2 = 2 initial profile (t = 0, with z0 < 0) Ex = E0 exp[-a (z-z0)^2] By = n1 * Ex Choose parameters so that the pulse width is at least a fact of 8 less than the z- domain of integration ( -L < z < L). For the slope of the interface, one could choose m = 1.Determine if the following formula is a tautology, contradiction, or neither. Show work. (p∧q)⟷(¬p∨¬q)Using MATLAB, develop a computer program for the finite difference solution with general θ scheme for the 1D consolidation of a uniform layer of soil. Compare the results for θ=0, 0.5, 2/3 and 1.0 for α=0.49 and α=0.51 against the analytical solution of Terzaghi’s equation for T=0.5. Apply the program to both cases of double draining layer and single draining layer.
- 4 We consider the following initial value problem for two functions y(x),z(x): 0=y′′+(y′+6y)cos(z), 5z′=x^2+y^2+z^2, where 0≤x≤2 and y(0)=1.7, y′(0)=−2.7, z(0)=0.5. Approximate the solution of this initial value problem using Euler's method with 100 steps. (use Maple) What approximation value do you obtain for y(2) and z(1)?Show complete solution please. I am at a loss of how to even begin this problem and would like to learn how to work through each step.Derive the solution of 0-1knapsack problem using the four steps of dynamic programming . Explain one example also.