Advanced Optimisation with Python • Course introduction • Canvas • Syllabus • Assessments • Software Copyright By PowCoder代写 加微信 powcoder • Optimisation • Quick refresher • Solving MIPs Course Introduction • Formulate and solve real-life problems using solvers • Mixed Integer Programming (MIP) • Develop computational optimisation skills to tackle complex problems • Column generation […]
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STAT 513/413: Lecture 2 And now to computing (starting with R) Computing environment (≈ language) Long ago: machine code → assembly language Programming languages: Fortran, Pascal, C(++), Java First time a bit comfortable: Matlab (Octave?) First dedicated for data-analysis: Lisp(-Stat) Very fashionable now: Python Dedicated for data-analysis: S → S-Plus → R Our choice: R
STAT 513/413: Lecture 4 Mostly linear algebra (first non-trivialities perhaps) A tale of expert code I: floating point arithmetics Floating-point arithmetics: numbers are represented as base ∗ 10exponent – which has inevitable consequences > 0.000001*1000000 [1] 1 > x=0; for (k in (1:1000000)) x=x+0.000001 >x [1] 1 > x-1 [1] 7.918111e-12 > x=1000000; for (k
CS代考计算机代写 matlab flex algorithm STAT 513/413: Lecture 4 Mostly linear algebra Read More »
STAT 513/413: Lecture 5 Another bit of linear algebra (solving equations) Recall Lecture 4: solving equations The theory for a linear model y ∼ Xβ may suggest to obtain the least squares estimates via the formula b = (XTX)−1XTy but experts in numerical computations know that it should be done rather via solving (the system
CS代考计算机代写 matlab chain STAT 513/413: Lecture 5 Another bit of linear algebra Read More »
ECE5884 Wireless Communications Quiz 8 Due: 19th September 2022 1. A channel 𝐡 is defined by its impulse response with h[0] = 0.5, h[1] = 𝑗/2, and h[2] = 0.4𝑒𝑥𝑝(𝑗𝜋/5). Use Matlab to calculate the Least Squares Equalizer of length 𝐿𝑓 = 5. The LS Equalizer is given by squared error corresponding to a delay
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CSI4130*[A] Computer Graphics 20211 – Exercises 1. Questions 2. Matlab/Octave Solution 3. Questions 4. Matlab/Octave Solution 5. Questions 6. Matlab/Octave Solution
CS代考计算机代写 matlab CSI4130*[A] Computer Graphics 20211 – Exercises Read More »
fprintf( ‘————————————-\n’ ); fprintf( ‘Q 1\n’ ); fprintf( ‘————————————-\n’ ); fprintf( ‘Q 1.1\n’ ); M_scale = [ 3 0; 0 2 ]; fprintf( ‘Scaling: \n’) % Transpose just to fix matlab printing fprintf( ‘%5.2f %5.2f\n’, M_scale’ ); fprintf( ‘\n\n’) fprintf( ‘Q 1.2\n’ ); H_x = [ 1 2; 0 1 ]; fprintf( ‘Shear x: \n’)
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0 Notes CSI4130 Review Questions – 2D Transformations University of Ottawa – Universit ́e d’Ottawa Jochen Lang These questions are meant as a review of lecture material. The style of these questions is not necessarily a good indication of the style of the midterm (see the midterm examples instead). 1 2D Transformation 1.1 Scaling Give
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Description ECE 438/538 Project 3 Due Friday 3/4, 11:59 PM Recycle the BEV of your assignment from the last project and complete the following: Copyright By PowCoder代写 加微信 powcoder 1) Size the battery pack (i.e., number of series and number of parallel) for your vehicle using all 2.2 Ah Li- ion cells. Assume the nominal
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Concise Machine Learning Jonathan Richard Shewchuk May 26, 2020 Department of Electrical Engineering and Computer Sciences University of California at Berkeley Berkeley, California 94720 Abstract This report contains lecture notes for UC Berkeley’s introductory class on Machine Learning. It covers many methods for classification and regression, and several methods for clustering and dimensionality reduction. It