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Experiments with the Python Imaging Library

July 14, 2012

Back in February 2009, I spent some time playing with the Python Imaging Library (PIL). I even started a blog post on my experiments with PIL, but I never finished it. I will finish it now. Be warned: this post will have a very experimental flavor.

Since we are all acquainted with Lena, as a test image I will use the following photograph of the late Ruslana Korshunova (1987-2008):

The flowers and the hair create plenty of high-frequency content. Let us now use the Python Imaging Library (PIL) to convert the image to B&W, invert the image, and apply several elementary filters to it.

__________

Python script

The following Python script opens the image above and performs the aforementioned operations on it:

import Image
import ImageChops
import ImageFilter

# open image and print format
Im = Image.open('Ruslana.jpg')
print Im.format, Im.size, Im.mode

# convert to black & white
ImBW = Im.convert('1')
ImBW.save('Ruslana_BW.bmp',"BMP")

# invert image (obtain negative)
ImInv = ImageChops.invert(Im)
ImInv.save('Ruslana_Inv.jpg',"JPEG")

# apply BLUR filter
ImBlur = Im.filter(ImageFilter.BLUR)
ImBlur.save('Ruslana_BLUR.jpg',"JPEG")

# apply CONTOUR filter
ImContour = Im.filter(ImageFilter.CONTOUR)
ImContour.save('Ruslana_CONTOUR.jpg',"JPEG")

# apply DETAIL filter
ImDetail = Im.filter(ImageFilter.DETAIL)
ImDetail.save('Ruslana_DETAIL.jpg',"JPEG")

# apply EDGE_ENHANCE filter
ImEH = Im.filter(ImageFilter.EDGE_ENHANCE)
ImEH.save('Ruslana_EDGE_ENHANCE.jpg',"JPEG")

# apply EDGE_ENHANCE_MORE filter
ImEHM = Im.filter(ImageFilter.EDGE_ENHANCE_MORE)
ImEHM.save('Ruslana_EHM.jpg',"JPEG")

# apply EMBOSS filter
ImEmb = Im.filter(ImageFilter.EMBOSS)
ImEmb.save('Ruslana_EMBOSS.jpg',"JPEG")

# apply FIND_EDGES filter
ImEdges = Im.filter(ImageFilter.FIND_EDGES)
ImEdges = ImEdges.save('Ruslana_FIND_EDGES.jpg',"JPEG")

# apply SMOOTH filter
ImSmooth = Im.filter(ImageFilter.SMOOTH)
ImSmooth = ImSmooth.save('Ruslana_SMOOTH.jpg',"JPEG")

# apply SMOOTH_MORE filter
ImSmoothMore = Im.filter(ImageFilter.SMOOTH_MORE)
ImSmoothMore = ImSmoothMore.save('Ruslana_SMOOTH_MORE.jpg',"JPEG")

# apply SHARPEN filter
ImSharp = Im.filter(ImageFilter.SHARPEN)
ImSharp = ImSharp.save('Ruslana_SHARPEN.jpg',"JPEG")

Let us now see how the processed images look like.

__________

Results

Here’s the B&W image:

The script produced a monochromatic (1 bit per pixel) BMP file. Since WordPress.com does not allow one to upload BMP files, I converted it to JPEG, which greatly increased the size of the file. How ironic! In this case, lossy compression actually led to expansion!

The inverted (i.e., negative) image looks quite interesting:

It looks as though the EDGE_ENHANCE_MORE filter performs some form of amplification of the high-frequency content of the image:

Look how funny the flowers and her hair look! If we want to extract the actual edges, we can use the FIND_EDGES filter:

The other images produced by the Python script are not particularly interesting and, therefore, I have not posted them here.

__________

Concluding remarks

The Python Imaging Library (PIL) offers the capability to perform some very basic image processing. However, given Python’s lack of a matrix data structure, one cannot do that much. I believe the PIL is useful to process large collections of photos (to generate thumbnails, for example), not to perform 2-dimensional signal processing.

__________

Batch Image Processing with Python

Tags:Digital Image Processing, Image Processing, Python
Posted in Python | 8 Comments »

Deciding the convexity of semialgebraic sets II

June 23, 2012

Let $S_3 \subset \mathbb{R}^{3 \times 3}$ denote the set of real symmetric $3 \times 3$ matrices. We introduce a matrix-valued function $A: \mathbb{R}^3 \to S_3$ defined by

$A (x) = \left[\begin{array}{ccc} 1 & x_1 & x_2\\ x_1 & 1 & x_3\\ x_2 & x_3 & 1\end{array}\right]$

The elliptope $E_3 \subset \mathbb{R}^3$ is defined by

$E_3 = \left\{ x \in \mathbb{R}^3 \mid A (x) \succeq 0 \right\}$

where $A (x) \succeq 0$ means that matrix $A (x)$ is positive semidefinite. Since the elliptope $E_3$ is defined by the linear matrix inequality (LMI) $A (x) \succeq 0$ , we can conclude that $E_3$ is a spectrahedron and, therefore, we have that $E_3$ is convex.

The $E_3$ elliptope (also known as “inflated tetrahedron”) is the yellow part (surface and interior) of the Cayley’s cubic surface plotted below

[ source ]

__________

$E_3$ is a semialgebraic set

Saying that $A (x)$ is positive semidefinite is equivalent to saying that the $2^3 - 1 = 7$ principal minors of $A (x)$ are nonnegative. If $A (x) \succeq 0$ , then the three 1st order principal minors (which are the diagonal entries of $A (x)$ ) lead to the following three (admittedly uninteresting) inequalities

$1 \geq 0$ , $1 \geq 0$ , $1 \geq 0$

which we can discard. The three 2nd order principal minors (determinants of $2 \times 2$ submatrices of $A (x)$ ) and the 3rd order principal minor (the determinant of $A (x)$ ) can be computed using the following Python 2.5 + SymPy script:

from sympy import *

# symbolic variables
x1 = Symbol('x1')
x2 = Symbol('x2')
x3 = Symbol('x3')

# build matrix A(x)
A = Matrix([[1,x1,x2],
            [x1,1,x3],
            [x2,x3,1]])

print "\nMatrix A(x):"
print A

print "\n2nd order principal minors:"
print A.extract([0,1],[0,1]).det()
print A.extract([0,2],[0,2]).det()
print A.extract([1,2],[1,2]).det()

print "\n3rd order principal minor:"
print A.det()

which produces the following output:

Matrix A(x):
[ 1, x1, x2]
[x1,  1, x3]
[x2, x3,  1]

2nd order principal minors:
-x1**2 + 1
-x2**2 + 1
-x3**2 + 1

3rd order principal minor:
-x1**2 + 2*x1*x2*x3 - x2**2 - x3**2 + 1

Hence, the 2nd order principal minors yield the following inequalities

$1 - x_1^2 \geq 0$ , $1 - x_2^2 \geq 0$ , $1 - x_3^2 \geq 0$

which define the $3$ -dimensional cube $[-1,1]^3 \subset \mathbb{R}^3$ . The 3rd order principal minor (i.e., the determinant) yields the inequality

$1 + 2 x_1 x_2 x_3 - (x_1^2 + x_2^2 + x_3^3) \geq 0$ .

We can thus conclude that $E_3$ is a basic closed semialgebraic set.

__________

Deciding the convexity of $E_3$

In my last post, I used quantifier elimination to decide the convexity of two semialgebraic sets in $\mathbb{R}^2$ . Let us now decide the convexity of $E_3$ , which is of higher “complexity” than the sets I considered last time. What is the goal? The goal is to find out whether REDLOG can decide the convexity of $E_3$ in less than 10 minutes.

Based on the scripts in my previous post, we write the following REDUCE + REDLOG script to decide the convexity of $E_3$ :

% decide the convexity of the elliptope E3

load_package redlog;
rlset ofsf;

% define predicates in antecedent
p1 := (1-x1^2>=0) and (1-x2^2>=0) and (1-x3^2>=0) and 
      (1-x1^2-x2^2-x3^2+2*x1*x2*x3>=0);
p2 := (1-y1^2>=0) and (1-y2^2>=0) and (1-y3^2>=0) and 
      (1-y1^2-y2^2-y3^2+2*y1*y2*y3>=0);
p3 := (theta>=0) and (theta<=1);

% define predicate in the consequent
z1 := theta*x1+(1-theta)*y1;
z2 := theta*x2+(1-theta)*y2;
q  := (1-z1^2>=0) and (1-z2^2>=0) and (1-z3^2>=0) and 
      (1-z1^2-z2^2-z3^2+2*z1*z2*z3>=0);

% define universal formula
phi := all({x1,x2,x3,y1,y2,y3,theta}, (p1 and p2 and p3) impl q);

% perform quantifier elimination
rlqe phi;

end;

After 30 minutes (!!!) waiting for the decision procedure to terminate, I killed the reduce.exe process (which was eating all my machine’s CPU cycles and RAM memory). This is disappointing…

Tags:Convex Algebraic Geometry, Convexity, Decision Problems, Linear Matrix Inequalities, Python, Quantifier Elimination, REDLOG, REDUCE, Semialgebraic Geometry, Semialgebraic Sets, Spectrahedra, SymPy
Posted in Algebraic Geometry, Python | Leave a Comment »

Fair play

March 31, 2012

Last February’s Ponder This challenge was the following:

To decide who would play first in a human vs. computer game, the human suggested to flip a coin it produced from its pocket, stating “heads you win, tails I win.” The computer replied, “Humans are very devious. I know that your coin is not fair, and that the probability of heads on that coin is slightly less than one half. To make this perfectly fair, flip the coin repeatedly, and I win if the coin first shows heads on toss number 1, 14, 15, 19, 20, or 23. You win if it first shows heads on any other toss.”

If the computer is correct and $p$ is the probability that the coin shows heads, give the best rational approximation to $p$ with a denominator having fewer than 10 digits.

Here is the solution.

__________

My solution #1:

Let $\mathbb{P} (H) = p$ be the probability that the coin will land with the heads side up. If the coin is biased in favor of the human player, then $p < 1/2$ . The probability that the coin toss will first produce “heads” on the $n$ -th trial (where $n \geq 1$ ) is $(1-p)^{n-1} p$ . Let us define $\mathcal{I} := \{1, 14, 15, 19, 20, 23\}$ . The probability that the computer will win is thus

$\displaystyle \sum_{n \in \mathcal{I}} (1-p)^{n-1} p$ .

We now assume that the computer knows the exact value of $p$ and, thus, is able to compute its probability of winning. Since the computer is interested in making this game “perfectly fair”, then his probability of winning should equal the human’s probability of winning, i.e., it should equal $1/2$ . Hence, we obtain the equation

$p + (1-p)^{13} p + (1-p)^{14} p + (1-p)^{18} p + (1-p)^{19} p +(1-p)^{22} p = \frac{1}{2}$

which we can solve using the following Python + SymPy script:

from sympy import *

# create symbolic variable
p = Symbol('p')

# solve polyomial equation
f = p + p*(1-p)**13+p*(1-p)**14+p*(1-p)**18+p*(1-p)**19+p*(1-p)**22
Solutions = solve(f-0.5,p)

# find (purely) real solutions
RealSols = filter(lambda z: im(z)==0, Solutions)

# print solutions
print "\nAll solutions:"
for sol in Solutions:
    print sol

# print real solutions
print "\nReal solutions:"
for sol in RealSols:
	print sol

This script outputs the following list of solutions:

All solutions:
0.334938061741437 - 1.03291350457235*I
0.334938061741437 + 1.03291350457235*I
1.97217279050061 + 0.143849563303172*I
1.50662666735084 - 0.797180194029411*I
1.92419089868505 + 0.454390584787364*I
0.0780115260218319 + 0.464383156442821*I
0.00197767511752256 - 0.101887310197153*I
1.27721993255011 + 0.951650156864179*I
1.27721993255184 - 0.951650156863701*I
0.247036637335883 - 0.624592516289942*I
0.625456509765409 - 0.845921918004056*I
0.0780115260218319 - 0.464383156442821*I
0.00197767511752256 + 0.101887310197153*I
0.991590970537258 - 0.923668626671437*I
1.79082570892033 - 0.599036471538201*I
1.92419089870934 - 0.454390584808249*I
1.79082570891289 + 0.599036471546724*I
0.499905242928390
0.6254565097654 + 0.845921918004058*I
0.991590970537348 + 0.923668626671824*I
0.247036637335883 + 0.624592516289942*I
1.50662666734992 + 0.797180194030292*I
1.97217279048996 - 0.14384956326771*I

Real solutions:
0.499905242928390

Hence, we have that the probability of heads is

$p = 0.499905242928390$ .

We can convert this number to a ratio of integers in Haskell using function approxRational in the library Data.Ratio:

import Data.Ratio

-- probability of heads
p = 0.499905242928390

-- create (infinite) list of epsilons
epsilons = map (10**) [-1,-2..]

-- create (infinite) list of ratios
ratios = map (approxRational p) epsilons

-- list of ratios whose denominators have less than 10 digits
ratios' = takeWhile (\x -> denominator x < 10^10) ratios

Running the script above in GHCi, we obtain a list of ratios:

*Main Data.Ratio> ratios'
[1 % 2,1 % 2,1 % 2,1 % 2,2386 % 4773,2611 % 5223,2636 % 5273,
2638 % 5277,13189 % 26383,44843 % 89703,166183 % 332429,
393036 % 786221,1405961 % 2812455,6243733 % 12489833,
14906352 % 29818355,69693988 % 139414397]

and we finally obtain the following rational approximation

$p \approx \displaystyle\frac{69693988}{139414397}$

whose denominator has nine digits. According to the webmaster, the rational approximation I presented above is not the best one. Hence, let us now try to obtain a better one.

__________

My solution #2:

Most likely, the reason why I did not obtain the best rational approximation is that the real solution I obtained was not precise enough. Hence, let us use the following MATLAB command to solve the polynomial equation:

solve('p + p*(1-p)^13+p*(1-p)^14+p*(1-p)^18+p*(1-p)^19+p*(1-p)^22 = 0.5')

This command produces the following list of solutions:

ans =

 .19776751175225606651411342151624e-2-.10188731019715301414082803578298*i
 .19776751175225606651411342151624e-2+.10188731019715301414082803578298*i
 .78011526021831885333432975572559e-1-.46438315644282071005250209738747*i
 .78011526021831885333432975572559e-1+.46438315644282071005250209738747*i
    .24703663733588276238861533742100-.62459251628994218281562354280746*i
    .24703663733588276238861533742100+.62459251628994218281562354280746*i
    .33493806174143698737516495548579-1.0329135045723528674007841220342*i
    .33493806174143698737516495548579+1.0329135045723528674007841220342*i
                                        .49990524292838950452962194522516
    .62545650976540488339104798827398-.84592191800405765193777444233924*i
    .62545650976540488339104798827398+.84592191800405765193777444233924*i
    .99159097053730697830960408610308-.92366862667162608535284662817351*i
    .99159097053730697830960408610308+.92366862667162608535284662817351*i
    1.2772199325509563050241902871670-.95165015686389448554291677056666*i
    1.2772199325509563050241902871670+.95165015686389448554291677056666*i
    1.5066266673486985232104204448828-.79718019402868789409470957216463*i
    1.5066266673486985232104204448828+.79718019402868789409470957216463*i
    1.7908257089204728277307542114304-.59903647154154201043833983995659*i
    1.7908257089204728277307542114304+.59903647154154201043833983995659*i
    1.9241908987061350990877558129099-.45439058479360516453156947604463*i
    1.9241908987061350990877558129099+.45439058479360516453156947604463*i
    1.9721727904901564352190617939256-.14384956327517564484908725760533*i
    1.9721727904901564352190617939256+.14384956327517564484908725760533*i

We now obtain a much more precise real solution

$p =0.49990524292838950452962194522516$ .

We modify the Haskell script, using the new value of $p$ and a much finer list of epsilons (the precisions):

import Data.Ratio

-- probability of heads
p = 0.49990524292838950452962194522516

-- create (infinite) list of epsilons
epsilons = map ((1.005)**) [-1,-2..]

-- create (infinite) list of ratios
ratios = map (approxRational p) epsilons

-- list of ratios whose denominators have less than 10 digits
ratios' = takeWhile (\x -> denominator x < 10^10) ratios

Which yields the following rational approximation

$p \approx \displaystyle\frac{61031369}{122085875}$

whose denominator has nine digits. Although this rational approximation does not match the one in the official solution, I still got my name on the “people who answered correctly” list. Surprisingly, the value of $p$ which I obtained using MATLAB is quite different from the value of $p$ used by the webmaster

$p = 0.499905242928506776611191007553$ .

Which value of $p$ is “more correct”? I do not know. Do you?

Tags:Data.Ratio, Haskell, Ponder This, Probability Theory, Puzzles, Python, SymPy
Posted in Haskell, Puzzles, Python | Leave a Comment »

Wythoff sequences

August 21, 2011

On November 20, 2007, the Putnam problem of the day was:

The set of all positive integers is the union of two disjoint subsets $\{f(1), f(2), f(3), \dots\}$ , $\{g(1), g(2), g(3), \dots\}$ , where $f(1) < f(2) < f(3) < \dots$ , and $g(1) < g(2) < g(3) < \dots$ , and $g(n) = f(f(n))+1$ for $n=1, 2, 3,\dots$ . Determine $f(240)$ .

Back in November 2007 I could not solve the problem. Now, almost four years later, I will give it another try.

__________

My “solution”:

Let $\mathbb{N} := \{1, 2, 3, \dots\}$ be the set of natural numbers (positive integers). We define $F := \{f(n)\}_{n \in \mathbb{N}}$ and $G := \{g(n)\}_{n \in \mathbb{N}}$ . Note that we must have $F \cup G = \mathbb{N}$ and $F \cap G = \emptyset$ .

Let $f(1) = 1$ . Then, we obtain

$g(1) = f(f(1)) + 1 = f(1) + 1 = 2$ .

Next, let $f(2) = 3$ and $f(3) = 4$ . Then,

$g(2) = f(f(2)) + 1 = f(3) + 1 = 5$ .

Let $f(4) = 6$ . Then, we obtain

$g(3) = f(f(3)) + 1 = f(4) + 1 = 7$ .

Next, let $f(5) = 8$ and $f(6) = 9$ . Then,

$g(4) = f(f(4)) + 1 = f(6) + 1 = 10$ .

Next, let $f(7) = 11$ and $f(8) = 12$ . Then,

$g(5) = f(f(5)) + 1 = f(8) + 1 = 13$ .

Let $f(9) = 14$ . Then, we obtain

$g(6) = f(f(6)) + 1 = f(9) + 1 = 15$ .

Next, let $f(10) = 16$ and $f(11) = 17$ . Then,

$g(7) = f(f(7)) + 1 = f(11) + 1 = 18$ .

Let $f(12) = 19$ . Then, we obtain

$g(8) = f(f(8)) + 1 = f(12) + 1 = 20$ .

Next, let $f(13) = 21$ and $f(14) = 22$ . Then,

$g(9) = f(f(9)) + 1 = f(14) + 1 = 23$ .

Let us stop iterating here. So far, we have

$\left(f(n)\right)_{n \in \mathbb{N}} = \left(1, 3, 4, 6, 8, 9, 11, 12, 14, 16, 17, 19, 21, 22, \dots\right)$

$\left(g(n)\right)_{n \in \mathbb{N}} = \left(2, 5, 7, 10, 13, 15, 18, 20, 23, \dots\right)$ .

Consulting the On-Line Encyclopedia of Integer Sequences (OEIS), we find out that $\left(f(n)\right)_{n \in \mathbb{N}}$ and $\left(g(n)\right)_{n \in \mathbb{N}}$ are the lower Wythoff (A000201) and upper Wythoff (A001950) sequences, respectively. These two are Beatty sequences generated by the golden ratio $\varphi := \frac{1 + \sqrt{5}}{2}$ , as follows

$f (n) = \lfloor n \varphi \rfloor$ , $g (n) = \lfloor n \varphi^2 \rfloor$

where $\lfloor \cdot \rfloor$ is the floor function. Recall that $\varphi^2 = \varphi + 1$ , which allows us to write $g (n) = \lfloor n (\varphi + 1) \rfloor$ .

The following Python script produces the first twenty terms of the lower and upper Wythoff sequences:

from math import floor
from math import sqrt

# golden ratio
phi = (1 + sqrt(5)) / 2.0

# define function f
def f (n):
    return int(floor(phi * n))

# define function g
def g (n):
    return int(floor((phi+1) * n))

print "Lower and upper Wythoff sequences:\n"
for n in range(1,21):
    print "f(%d) = %d \t g(%d) = %d" % (n, f(n), n, g(n))

This script produces the output:

Lower and upper Wythoff sequences:

f(1) = 1         g(1) = 2
f(2) = 3         g(2) = 5
f(3) = 4         g(3) = 7
f(4) = 6         g(4) = 10
f(5) = 8         g(5) = 13
f(6) = 9         g(6) = 15
f(7) = 11        g(7) = 18
f(8) = 12        g(8) = 20
f(9) = 14        g(9) = 23
f(10) = 16       g(10) = 26
f(11) = 17       g(11) = 28
f(12) = 19       g(12) = 31
f(13) = 21       g(13) = 34
f(14) = 22       g(14) = 36
f(15) = 24       g(15) = 39
f(16) = 25       g(16) = 41
f(17) = 27       g(17) = 44
f(18) = 29       g(18) = 47
f(19) = 30       g(19) = 49
f(20) = 32       g(20) = 52

Finally, we have $f (240) = \lfloor 240 \varphi \rfloor = 388$ .

__________

I am not happy with this “solution”. I obviously cheated when I consulted the OEIS. Supposing one is not acquainted with Beatty sequences, is there any hope of solving the problem? If one happens to be acquainted with Beatty sequences and postulates that

$f (n) = \lfloor n r \rfloor$ , $g (n) = \lfloor n s \rfloor$

where $\frac{1}{r} + \frac{1}{s} = 1$ , how does one arrive at the conclusion that $r = \varphi$ and $s = \varphi^2$ ? In other words, what would a proper solution look like?

Tags:Beatty Sequences, Golden Ratio, Integer Sequences, Putnam problem of the day, Python, Wythoff Sequences
Posted in Puzzles, Python | 1 Comment »

Sum of squares decomposition II

July 5, 2011

Last week, we concluded that the quartic form

$F(x, y) = 2 x^4 + 2 x^3 y - x^2 y^2 + 5 y^4$

has the following sum of squares decomposition

$F(x, y) = \displaystyle \frac{1}{2} \left(2 x^2 -3 y^2 + x y\right)^2 + \frac{1}{2} \left(y^2 + 3 x y\right)^2$

and, thus, $F$ is globally nonnegative. More generally, we saw that $F$ can be written as follows

$F(x,y) = \left[\begin{array}{c} x^2\\ y^2\\ x y\end{array}\right]^T \left[\begin{array}{ccc} 2 & -\lambda & 1\\ -\lambda & 5 & 0\\ 1 & 0 & 2 \lambda - 1\end{array}\right] \left[\begin{array}{c} x^2\\ y^2\\ x y\end{array}\right]$

where $\lambda \in \mathbb{R}$ is a parameter. Let us define $z := (x^2, y^2, x y)$ and

$Q := \left[\begin{array}{ccc} 2 & -\lambda & 1\\ -\lambda & 5 & 0\\ 1 & 0 & 2 \lambda - 1\end{array}\right]$

so that we can write polynomial $F$ in the more compressed form $F(x, y) = z^T Q z$ . A sufficient condition for the existence of a SOS decomposition is that $Q$ is positive semidefinite, i.e., $Q \succeq 0$ . Why? If $Q$ is positive semidefinite, then it has a Cholesky factorization $Q = L^T L$ and, thus, $F(x, y) = z^T Q z = z^T L^T L z = \| L z \|_2^2$ , which finally yields the SOS decomposition

$F(x, y) = \displaystyle \sum_{i =1}^3 \left(L_{i1} x^2 + L_{i2} y^2 + L_{i3} xy \right)^2$

where $L_{ij}$ is the $(i,j)$ entry of matrix $L$ . One can also think of the Cholesky factorization as $Q = B B^T$ , where $B = L^T$ , so that we obtain $F(x, y) = \| B^T z \|_2^2$ . However, let us not lose sight of where we are going. The question to be answered is: how do we find a $\lambda \in \mathbb{R}$ such that $Q \succeq 0$ ?

__________

Admissible values of $\lambda$

Saying that $Q$ is positive semidefinite is equivalent to saying that the $2^3 - 1 = 7$ principal minors of $Q$ are nonnegative. If $Q \succeq 0$ , then the three 1st degree principal minors (which are the diagonal entries of $Q$ ) lead to the three inequalities

$2 \geq 0$ , $5 \geq 0$ , $2 \lambda - 1 \geq 0$ .

The first two inequalities are redundant, and the third yields $\lambda \geq \frac{1}{2}$ . The three 2nd degree principal minors (determinants of $2 \times 2$ submatrices of $Q$ ) and the 3rd degree principal minor (determinant of $Q$ ) can be computed with the following Python / SymPy script:


from sympy import *

# symbolic variable
Lambda = Symbol('Lambda')

# build matrix Q
Q = Matrix([[2,-Lambda,1], [-Lambda,5,0], [1,0,2*Lambda-1]])

print "\n2nd degree principal minors:"
print Q.extract([0,1],[0,1]).det()
print Q.extract([0,2],[0,2]).det()
print Q.extract([1,2],[1,2]).det()

print "\n3rd degree principal minor:"
print Q.det().factor()

which produces the following output:

2nd degree principal minors:
-Lambda**2 + 10
4*Lambda - 3
10*Lambda - 5

3rd degree principal minor:
-(Lambda - 3)*(2*Lambda**2 + 5*Lambda - 5)

From the three 2nd degree principal minors, we then obtain the three inequalities

$- \lambda^2 + 10 \geq 0$ , $4 \lambda - 3 \geq 0$ , $10 \lambda - 5 \geq 0$

which are equivalent to

$\lambda^2 \leq 10$ , $\lambda \geq \frac{3}{4}$ , $\lambda \geq \frac{1}{2}$

which yields $\lambda \in [\frac{3}{4}, \sqrt{10}]$ . From the 3rd degree principal minor, we obtain the inequality

$- 2 \lambda^{3} + \lambda^{2} + 20 \lambda - 15 = - \left(\lambda -3\right) \left(2 \lambda^{2} + 5 \lambda -5\right) \geq 0$ .

Hence, we have that admissible values of $\lambda$ must satisfy

$\lambda \in [\frac{3}{4}, \sqrt{10}]$ , $\left(\lambda -3\right) \left(2 \lambda^{2} + 5 \lambda -5\right) \leq 0$ .

The roots of polynomial $2 \lambda^{2} + 5 \lambda -5$ are $(-5 \pm \sqrt{65}) / 4$ . Unfortunately $(-5 + \sqrt{65}) / 4$ is in $[\frac{3}{4}, \sqrt{10}]$ . Hence, let us partition the interval $[\frac{3}{4}, \sqrt{10}]$ into two intervals:

for $\lambda \in [\frac{3}{4}, \frac{-5 + \sqrt{65}}{4})$ , we have that $2 \lambda^{2} + 5 \lambda -5$ is negative, and thus $\left(\lambda -3\right) \left(2 \lambda^{2} + 5 \lambda -5\right) \leq 0$ implies that $\lambda \geq 3$ , but

$\{ \lambda \in \mathbb{R} : \lambda \in [\frac{3}{4}, \frac{-5 + \sqrt{65}}{4}) \land \lambda \geq 3\} = \emptyset$ .

for $\lambda \in [\frac{-5 + \sqrt{65}}{4}, \sqrt{10}]$ , we have that polynomial $2 \lambda^{2} + 5 \lambda -5$ is nonnegative, and thus $\left(\lambda -3\right) \left(2 \lambda^{2} + 5 \lambda -5\right) \leq 0$ implies that $\lambda \leq 3$ . Hence,

$\{ \lambda \in \mathbb{R} : \lambda \in [\frac{-5 + \sqrt{65}}{4}, \sqrt{10}] \land \lambda \leq 3\} = [\frac{-5 + \sqrt{65}}{4}, 3]$ .

Finally, we conclude that the set of admissible values of $\lambda$ is $\Lambda := [\frac{-5 + \sqrt{65}}{4}, 3]$ . Note that $\frac{-5 + \sqrt{65}}{4} \approx 0.765$ .

__________

An ensemble of SOS decompositions

Now that we know for which values of $\lambda$ matrix $Q$ is positive semidefinite, we can compute various SOS decompositions. The following Python / SymPy script computes an SOS decomposition for $\lambda = 3$ :


from sympy import *

# symbolic variables
Lambda = Symbol('Lambda')
x = Symbol('x')
y = Symbol('y')

# define polynomial
F = 2*(x**4) + 2*(x**3)*y - (x**2)*(y**2) + 5*(y**4)

# build matrix Q
Q = Matrix([[2,-Lambda,1], [-Lambda,5,0], [1,0,2*Lambda-1]])

# build vector of monomials
z = Matrix(3, 1, [x**2, y**2, x*y])

# compute Cholesky factorization
# for a particular value of Lambda
B = Q.subs(Lambda, Rational(3,1)).cholesky()

# obtain vector of SOS polynomials
f = B.T * z

print "\nSOS polynomials:"
pprint(f[0])
pprint(f[1])
pprint(f[2])

# compute residual polynomial
R = F - sum([f[i]**2 for i in [0,1,2]])

print "\nResidual polynomial: %s" % R.expand()

and it produces the SOS decomposition

$F(x, y) = \displaystyle \frac{1}{2} \left(2 x^2 -3 y^2 + x y\right)^2 + \frac{1}{2} \left(y^2 + 3 x y\right)^2$

which is the one we obtained last week. For $\lambda = 3/2$ , we get

$F(x, y) = \displaystyle \frac{1}{2} \left(2 x^2 - \frac{3}{2} y^2 + x y\right)^2 + 62 \left(\frac{1}{4} y^2 + \frac{3}{62} x y\right)^2 +\frac{1302}{961} x^2 y^2$

and to verify that this is correct, we can run the script:

h0 = Rational(1,2) * (2*x**2 - Rational(3,2)*y**2 + x*y)**2
h1 = 62 * (Rational(3,62)*x*y + Rational(1,4)*y**2)**2
h2 = Rational(1302,961)*x**2*y**2
pprint((F - (h0 + h1 + h2)).expand())

For $\lambda = 5/2$ , we obtain the SOS decomposition

$F(x, y) = \displaystyle \frac{1}{2} \left(2 x^2 - \frac{5}{2} y^2 + x y\right)^2 + \frac{30}{16} \left(y^2 + \frac{2}{3} x y\right)^2 +\frac{8}{3} x^2 y^2$ .

But, Доверяй, но проверяй!

h0 = Rational(1,2) * (2*x**2 - Rational(5,2)*y**2 + x*y)**2
h1 = Rational(30,16) * (Rational(2,3)*x*y + y**2)**2
h2 = Rational(8,3)*x**2*y**2
pprint((F - (h0 + h1 + h2)).expand())

__________

What happens for non-admissible values of $\lambda$ ?

We know that if $\lambda \in \Lambda$ , then $Q \succeq 0$ . What if $\lambda \notin \Lambda$ ? Then matrix $Q$ will be negative definite and will, or will not, have a Cholesky factorization, depending on what convention one uses.

One can think of matrices $L$ and $B$ in the Cholesky factorizations $Q = L^T L$ and $Q = B B^T$ , respectively, as square roots of matrix $Q$ . Do negative reals have square roots? They do if you are willing to work with complex numbers. Likewise, a negative definite $Q$ will have a Cholesky factorization if one is willing to work with complex matrices. For example, for $\lambda = 0$ we obtain the SOS decomposition

$F(x, y) = \displaystyle \sum_{i=0}^2 f_i^2 (x, y)$

where the $f_i$ polynomials are:

$f_0 (x,y) = \frac{\sqrt{2}}{2} (2 x^2 + x y)$ , $f_1 (x,y) = \sqrt{5} y^2$ , $f_2 (x,y) = i \frac{\sqrt{6}}{2} x y$

where $i := \sqrt{-1}$ . Note that $f_0, f_1 \in \mathbb{R}[x, y]$ , but $f_2 \in \mathbb{C}[x, y]$ . If we square $f_2$ , we get $f_2^2 = -\frac{3}{2} x^2 y^2$ . This term is being subtracted from the sum of squares, which prevents us from concluding global nonnegativity. That is why we impose the condition $Q \succeq 0$ .

Tags:Algebra, Global Nonnegativity, Polynomials, Real Algebraic Geometry, Sum of Squares, Sum of Squares Decomposition, SymPy
Posted in Algebra, Algebraic Geometry, Python | Leave a Comment »

Rod Carvalho

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Rod Carvalho

Archive for the ‘Python’ Category

Experiments with the Python Imaging Library

Deciding the convexity of semialgebraic sets II

Fair play

Wythoff sequences

Sum of squares decomposition II

Pages

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Top Posts

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Recent Comments

Tag Cloud

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