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solutions to exercises 1.26

Exercise: Find the determinants of Jacobi matrices with \(a=1,bc=-1\) and \(a=-1,bc=-1\).
Solution: First assume \(a=1,bc=-1\), and use the regression obtained in the lecture notes, i.e $$j_{k+1}=aj_k-bcj_{k-1} = j_k+j_{k-1}$$

Those are Fibonacci numbers with initial conditions obtained by the first two …

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1.26 determinants

\(\def\Real{\mathbb{R}}\def\Comp{\mathbb{C}}\def\Rat{\mathbb{Q}}\def\Field{\mathbb{F}}\def\Fun{\mathbf{Fun}}\def\e{\mathbf{e}}
\def\f{\mathbf{f}}\def\bv{\mathbf{v}}\def\i{\mathbf{i}}
\def\eye{\left(\begin{array}{cc}1&0\\0&1\end{array}\right)}
\def\bra#1{\langle #1|}\def\ket#1{|#1\rangle}\def\j{\mathbf{j}}\def\dim{\mathrm{dim}}
\def\ker{\mathbf{ker}}\def\im{\mathbf{im}}
\)

1. Determinant is a function of square matrices (can be defined for any operator \(A:U\to U\), but we’ll avoid this abstract detour). It can be defined as a function that has the following …

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1.24 linear operators

\(\def\Real{\mathbb{R}}\def\Comp{\mathbb{C}}\def\Rat{\mathbb{Q}}\def\Field{\mathbb{F}}\def\Fun{\mathbf{Fun}}\def\e{\mathbf{e}}
\def\f{\mathbf{f}}\def\bv{\mathbf{v}}\def\i{\mathbf{i}}
\def\eye{\left(\begin{array}{cc}1&0\\0&1\end{array}\right)}
\def\bra#1{\langle #1|}\def\ket#1{|#1\rangle}\def\j{\mathbf{j}}\def\dim{\mathrm{dim}}
\def\ker{\mathbf{ker}}\def\im{\mathbf{im}}
\)

1. Linear mappings, or linear operators are just linear functions taking values in a linear space:
\[
A:U\to V,\ \mathrm{where\ both\ } U\ \mathrm{and\ } V\ \mathrm{ are\ linear\ spaces.}
\]
The same …

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1.19 Linear spaces

\(\def\Real{\mathbb{R}}\def\Comp{\mathbb{C}}\def\Rat{\mathbb{Q}}\def\Field{\mathbb{F}}\def\Fun{\mathbf{Fun}}\def\e{\mathbf{e}}
\def\f{\mathbf{f}}\def\bv{\mathbf{v}}\def\i{\mathbf{i}}
\def\eye{\left(\begin{array}{cc}1&0\\0&1\end{array}\right)}
\def\bra#1{\langle #1|}\def\ket#1{|#1\rangle}\def\j{\mathbf{j}}\def\dim{\mathrm{dim}}
\def\ker{\mathbf{ker}}\def\im{\mathbf{im}}
\)

1. Linear (or vector) spaces (over a field \(k\) – which in our case will be almost always the complex numbers) are sets that satisfy the following standard list of properties:

  • There exists
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solutions to exercises 1.17

Exercise 1: describe the curve \(1/z\), where \(z=1+ti, ~~t\in (-\infty,\infty)\).


Solution: apply \(\frac{1}{z} = \frac{\bar z}{|z|^2}\) and get \( \frac{1}{1+ti} = \frac{1-ti}{1+t^2}\), which is a parametric curve  of the form \(p(t) = x(t)+iy(t)\) where \(x(t) = \frac{1}{1+t^2},~~~ y(t) = \frac{-t}{1+t^2}\).…

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1.17 Complex numbers

\(\def\Real{\mathbb{R}}\def\Comp{\mathbb{C}}\def\Rat{\mathbb{Q}}\def\Field{\mathbb{F}}\def\Fun{\mathbf{Fun}}\def\e{\mathbf{e}}\def\f{\mathbf{f}}\def\bv{\mathbf{v}}\def\i{\mathbf{i}}
\def\eye{\left(\begin{array}{cc}1&0\\0&1\end{array}\right)}
\)

Complex numbers are expressions \(z=x+\i y\), where \(\i\) is the imaginary unit, defined by \(\i^2=-1\). Note the ambiguity (\(-\i\) would work just as well).

Complex numbers can be added subtracted, multiplied the usual way, with the …

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JACT

coming soon.…

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mock final

Here. (Disregard the points for the problems.)final_mock

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week 13

\(\def\Real{\mathbb{R}}
\def\Comp{\mathbb{C}}
\def\Rat{\mathbb{Q}}
\def\Field{\mathbb{F}}
\def\Fun{\mathbf{Fun}}
\def\e{\mathbf{e}}
\def\f{\mathbf{f}}
\def\bv{\mathbf{v}}
\)

Interior point methods

Logarithmic barriers. Central path. Convergence and quality of solution estimates from duality. (Boyd, chapt.11).

Ellipsoid method

(Use Boyd’s notes).


Sample problems

  • Find the central path for the
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week 12

\(\def\Real{\mathbb{R}}
\def\Comp{\mathbb{C}}
\def\Rat{\mathbb{Q}}
\def\Field{\mathbb{F}}
\def\Fun{\mathbf{Fun}}
\def\e{\mathbf{e}}
\def\f{\mathbf{f}}
\def\bv{\mathbf{v}}
\)

Conjugate gradient method

Material:Guler, ch. 14.7-9

Conjugate directions. Gram-Schmidt algorithm. Conjugate directions algorithm. Performance estimates via spectral data.


Sample problems

  • Consider the 4-dimensional space spanned by the polynomials
    \[
    p(x)=ax^3+bx^2+cx+d
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