Minimize Conjugate-Gradient

b935f1bf-61ec-45e3-8f45-aa4ae374918f 12 15 12 decimal

&[DATE] &[TIME] - &[FILENAME]

data char size clr plot k 1 data rows range r3 k el char : r4 k el size : r5 k el clr : 13 mat for data r3 r4 r5 augment 21 line :

init iter Minimize XY f φ β LS X XY 1 col : Y XY 2 col : n XY rows : k x β φ length 1 - : vx-> raw initial vector fit i 1 X length range vx i el X i el β f eval : for M-> Cholesky GradientMatrix j 1 k range i 1 n range M i j el X i el β φ j 1 + el eval : for for CholeskySolver LeastSquares Φ M transpose M * : d M transpose Y vx - (* : Δβ Φ 1 -^eval d * : b β Δβ + : 131 line : β 1 el init : i 1 : i iter ≤ β i 1 + el XY f φ β i el LS : i i 1 + : 21 line while β β iter el : 41 line β 31 line :

Vector Gradient ... Optimiz Symbolic
seemingly a scalar kernel  not 
recognized in program loop x β f n φ ct 1 : i 1 n range PD ct el x β f (β ct el diff : stack f(x,β) & PD V 1 el x β f : V i 1 + el PD i el : 21 line ct ct 1 + : 41 line for V 31 line :

Error stem plot for QuickPlot unicolor data Error stem 0 : stemX 1 data rows range : i 1 stemX rows range stem i el data i 1 el data i 2 el data i 1 el 022 mat : for i 1 stem rows 1 - (range stem i el stem i el stem i 1 + (1 el 1 el 012 mat stack : for V stem 1 el : j 2 stem rows 1 - (range V V stem j el stack : for V V data data rows row stack : 81 line :

L H N xd U 0 : dx H L - N / : i 1 N 1 + range U i el L dx - (dx i * + : for U 41 line :

Bar plot unicolor style, bar width user. data dt BarSize v data 2 col : t v bar t dt - 0 t dt - v t dt + v t dt + 042 mat : j 1 data rows range j 1 ≡ B data j 1 el data j 2 el bar : B B data j 1 el data j 2 el bar stack : if 11 line for B 41 line :

corr Pearson correlation χ X Mean : μ Y Mean : n XY rows : 13 mat X i el χ - (Y i el μ - (* (i 1 n sum X i el χ - (2^(i 1 n sum Y i el μ - (2^(i 1 n sum * sqrt / X i el χ - (X i el β f eval μ - (* (i 1 n sum X i el χ - (2^(i 1 n sum X i el β f eval μ - (2^(i 1 n sum * sqrt / / 31 line :

correlation between [X,Y data]

correlation ... [X,fitted model]

Absolute  residuals observer
fetches β from calculated δ j 1 XY rows range Δ j el Y j el X j el β f - eval : for X Δ augment 21 line :

SSD = "Sum Square Differences" SSD Y i el X i el β f - (2^(i 1 XY rows sum :

Otherwise Pearson independent of f(x,ParameterName) x y corr n x length : xy x y * vectorize : x2 x x * vectorize : y2 y y * vectorize : n xy sum (* x sum (y sum (* - n x2 sum (* x sum (2^- (n y2 sum (* y sum (2^- (* (sqrt / 51 line :

Fit.Y ii 1 n range Fit ii el X ii el β f : for Fit 21 line :

XY

XY 0.013 12 0.038 57 0.063 96 0.088 91 0.113 83 0.138 97 0.163 111 0.188 114 0.213 109 0.238 142 0.263 111 0.288 139 0.313 103 0.338 119 0.363 127 0.388 118 0.413 156 0.438 137 0.463 133 0.488 125 0.513 101 0.538 105 0.563 116 0.588 110 0.613 94 0.638 104 0.663 90 0.688 87 0.713 84 0.738 73 0.763 87 0.788 82 0.813 68 0.838 70 0.863 65 0.888 66 0.913 51 0.938 49 0.963 48 0.988 44 1.013 41 1.038 39 1.063 38 1.088 37 1.113 32 1.138 35 1.163 30 1.188 29 1.213 23 1.238 30 1.263 21 1.288 23 1.313 26 1.338 23 1.363 24 1.388 19 1.413 14 1.438 17 1.463 13 1.488 22 1.513 12 1.538 14 1.563 10 1.588 18 1.613 14 1.638 13 1.663 10 1.688 14 1.713 8 1.738 8 1.763 4 1.788 8 1.813 6 1.838 9 1.863 10 1.888 9 1.913 3 1.938 2 1.963 8 1.988 4 2.013 4 2.038 10 2.063 4 2.088 3 2.113 1 2.138 5 2.163 2 2.188 5 2.213 3 2.238 4 2.263 7 2.288 9 2.313 1 2.338 1 2.363 5 2.388 6 2.413 3 2.438 5 2.463 3 2.488 2 2.513 1 2.538 1 2.563 2 2.588 1 2.613 4 2.638 4 2.663 1 2.688 3 2.713 4 2.738 4 2.763 4 2.788 1 2.813 4 2.838 0 2.863 2 2.888 4 2.913 2 2.938 0 2.963 0 2.988 0 3.013 1 3.038 2 3.063 1 3.088 2 3.113 0 3.138 0 3.163 1 3.188 0 3.213 0 3.238 0 3.263 1 3.288 0 3.313 1 3.338 0 3.363 1 3.388 3 3.413 1 3.438 0 3.463 1 3.488 0 3.513 0 3.538 1 3.563 1 3.588 3 3.613 0 3.638 0 3.663 1 3.688 0 3.713 0 3.738 0 3.763 0 3.788 0 3.813 1 3.838 1 3.863 0 3.888 1 3.913 0 3.938 2 3.963 1 3.988 2 4.013 0 4.038 0 4.063 1 4.088 1 4.113 0 4.138 0 4.163 1 4.188 0 4.213 0 4.238 1 4.263 0 4.288 0 4.313 0 4.338 0 4.363 1 4.388 0 4.413 0 4.438 1 4.463 0 4.488 0 4.513 1 4.538 0 4.563 0 4.588 0 4.613 1 4.638 0 4.663 0 4.688 1 4.713 0 4.738 0 4.763 0 4.788 0 4.813 0 4.838 0 4.863 0 4.888 0 4.913 1 4.938 0 4.963 1 4.988 12002 mat :

The user PDF model x β f β 1 el x * β 2 el x * exp * :

X XY 1 col : Y XY 2 col : 12 mat data XY . 8 black plot : n XY rows : 31 line

x β φ x β f 2 φ :

n 200

init 10003 - 12 mat transpose :

iter 3 :

algo solving for β β init iter Minimize :

assign/isolate .. β β :

SSD 6845

β 1146.0242 3.1887 - 21 mat

β 1146.0242 3.1887 - 21 mat :

corr 1.0041

Absolute residuals δ i 1 XY rows range Δ i el Y i el X i el β f - 0 round eval : for X Y Δ augment 21 line :

fit U 05 0.025 range : i 1 U rows range fit i el 1 U i el * β f : for U fit augment 31 line :

px 0.3125 :

x c 10 x ≤ (x 10 ≤ (& cases :

data x β f x c * px 200 px 25 - 2 2 mat 3 1 sys

Δelta X δ 3 col augment : stem Δelta Error : 21 line

0 stem px 50 px 50 - 2 2 mat 3 1 sys