## Posts Tagged ‘Real Algebraic Geometry’

### Deciding the convexity of semialgebraic sets

June 16, 2012

Given a basic closed semialgebraic set [1], $S \subset \mathbb{R}^n$, defined by

$S = \{ x \in \mathbb{R}^n \mid g_1 (x) \geq 0 \land \dots \land g_m (x) \geq 0\}$

where $m \in \mathbb{N}$ and $g_1, \dots, g_m \in \mathbb{R}[x]$, how can one decide if set $S$ is convex? Can one use quantifier elimination?

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Convexity

Let us recall the definition of convexity [2]:

Definition: A set $S$ is convex if the line segment between any two points in $S$ lies in $S$, i.e., if for any $x, y \in S$ and any $\theta$ with $0 \leq \theta \leq 1$, we have $\theta x + (1-\theta) y \in S$.

Let us introduce a predicate $p : \mathbb{R}^n \to \{\text{True}, \text{False}\}$, defined by

$\displaystyle p (x) = \bigwedge_{i = 1}^m g_i (x) \geq 0$

so that we can write $S$ in the more parsimonious form

$S = \{ x \in \mathbb{R}^n \mid p (x) \}$.

Hence, writing $x \in S$ is equivalent to asserting that $p (x) = \text{True}$. From the definition above, it follows that set $S$ is convex if and only if the following universally quantified formula

$\forall x \, \forall y \, \forall \theta \, \left[ \, p(x) \land p(y) \land (\theta \geq 0 \land \theta \leq 1) \implies p (\theta x + (1-\theta) y) \, \right]$

where $x, y$ range over $\mathbb{R}^n$ and $\theta$ ranges over $\mathbb{R}$, evaluates to $\text{True}$. Since $g_1, \dots, g_m$ are polynomials, the formula above can be decided using quantifier elimination software like QEPCAD or REDLOG. Unfortunately, decidability does not imply that real quantifier elimination will decide the formula in an expedite manner. Let us now consider two instances of the decision problem under study.

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Example #1

Consider the following semialgebraic set

$S_1 := \{ x \in \mathbb{R}^2 \mid x_1 - x_2^2 + 3 \geq 0 \land -x_1 - x_2^2 + 3 \geq 0\}$

which we depict below

Visual inspection of the plot above allows us to conclude that set $S_1$ is convex. However, relying on the human visual system is extremely limiting. Therefore, we would like to automate the decision. We decide the convexity of $S_1$ via quantifier elimination using the following REDUCE + REDLOG script:

% decide the convexity of a semialgebraic set

load_package redlog;
rlset ofsf;

% define predicates in antecedent
p1 := (x1-x2^2+3>=0) and (-x1-x2^2+3>=0);
p2 := (y1-y2^2+3>=0) and (-y1-y2^2+3>=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  := (z1-z2^2+3>=0) and (-z1-z2^2+3>=0);

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

% perform quantifier elimination
rlqe phi;

end;

This script returns the truth value $\text{True}$ and, hence, $S_1$ is, indeed, convex. However, it took REDLOG approximately 60 seconds to decide the convexity of $S_1$, which is considerably longer than any of my previous experiments with REDLOG. Since we are performing quantifier elimination on a formula with five universal quantifiers, I am not incredibly surprised that it takes a while to obtain an answer.

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Example #2

Consider now the following semialgebraic set

$S_2 := \{ x \in \mathbb{R}^2 \mid x_1 - x_2^2 + 3 \geq 0 \land x_1 - x_2^2 + 1 \leq 0\}$

part of which we depict below

Visual inspection of the plot above allows us to conclude that set $S_2$ is not convex. We decide the convexity of $S_2$ using the following REDUCE + REDLOG script:

% decide the convexity of a semialgebraic set

load_package redlog;
rlset ofsf;

% define predicates in antecedent
p1 := (x1-x2^2+3>=0) and (x1-x2^2+1<=0);
p2 := (y1-y2^2+3>=0) and (y1-y2^2+1<=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  := (z1-z2^2+3>=0) and (z1-z2^2+1<=0);

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

% perform quantifier elimination
rlqe phi;

end;

This script returns the truth value $\text{False}$ and, hence, $S_2$ is, indeed, not convex. Interestingly, it took REDLOG less than one second to decide the formula. That is over two orders of magnitude faster than in example #1.

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References

[1] Markus Schweighofer, Describing convex semialgebraic sets by linear matrix inequalities, International Symposium on Symbolic and Algebraic Computation, Korea Institute for Advanced Study, Seoul, July 2009.

[2] Stephen Boyd, Lieven Vandenberghe, Convex Optimization.

### Deciding 2-colorability via quantifier elimination

September 1, 2011

A vertex coloring of a graph $G = (V,E)$ is a map $\kappa : V \to C$ that assigns a color to each vertex of $G$. The coloring is proper if adjacent vertices are assigned distinct colors. The graph is 2-colorable if there exists a proper coloring whose color set $C$ has two elements.

For instance, consider a graph $G = (V,E)$ whose vertex set is $V := \{1, 2, 3\}$ and whose edge set is $E := \{\{1,2\}, \{1,3\}, \{2,3\}\}$. A pictorial representation of this graph is depicted below

Note that this graph is complete. We can assign two distinct colors to any two vertices, but we cannot assign a color to the third vertex that will yield a proper coloring. Therefore, the graph is not 2-colorable. To illustrate, we use the color set $C := \{\text{pink}, \text{blue}\}$ and generate all $2^3 = 8$ possible vertex 2-colorings, as depicted below

Notice that none of these 2-colorings is proper, as expected.

As discussed by De Loera et alia [1], the graph vertex-coloring problem can be modeled using polynomial equations. We introduce variables $x_1, x_2, x_3$, where $x_i = -1$ if vertex $i$ is assigned the color $\text{pink}$, and $x_i = 1$ if vertex $i$ is assigned the color $\text{blue}$. Given an edge $\{i,j\} \in E$, we have that:

• if both vertices are pink, we get $x_i + x_j = -2$.
• if both vertices are blue, we get $x_i + x_j = 2$.
• if the vertices have distinct colors, we get $x_i + x_j = 0$.

Thus, stating that adjacent vertices must be assigned distinct colors is equivalent to imposing the constraints $x_i + x_j = 0$ for all $\{i,j\} \in E$. We thus obtain a system of three equations

$\begin{array}{rl} x_1 + x_2 &= 0\\ x_1 + x_3 &= 0\\ x_2 + x_3 &= 0\end{array}$

where $x_1, x_2, x_3 \in \{-1,1\}$. This is still a combinatorial problem, though. We can transform it into an algebraic-geometric problem. Here is a truly beautiful trick [1]: the constraint $x_i \in \{-1,1\}$ can be encoded as $x_i^2 = 1$ with $x_i \in \mathbb{R}$. How quaint!! We then obtain a system of six equations with polynomials in $\mathbb{R}[x_1, x_2, x_3]$

$\begin{array}{rl} x_1 + x_2 &= 0\\ x_1 + x_3 &= 0\\ x_2 + x_3 &= 0\\ x_1^2 - 1 &= 0\\ x_2^2 - 1 &= 0\\ x_3^2 -1 &= 0\end{array}$

whose solution set, which we denote by $S$, is algebraic [2]. The given graph is 2-colorable if and only if the system of polynomial equations above is feasible, i.e., if set $S$ is nonempty [1]. The following REDUCE + REDLOG script uses quantifier elimination to decide if $S$ is nonempty:

% feasibility via quantifier elimination

load_package redlog;
rlset ofsf;

% define polynomial functions
f1 := x1+x2;
f2 := x1+x3;
f3 := x2+x3;
f4 := x1^2-1;
f5 := x2^2-1;
f6 := x3^2-1;

% define existential formula
phi := ex({x1,x2,x3}, f1=0 and f2=0 and f3=0 and
f4=0 and f5=0 and f6=0);

% perform quantifier elimination
rlqe phi;

end;

This script produces the truth value false. Thus, the solution set $S$ is empty, which means that the system of polynomial equations is infeasible. We conclude that the given graph is not 2-colorable.

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References

[1] Jesús De Loera, Jon Lee, Susan Margulies, Shmuel Onn, Expressing Combinatorial Optimization Problems by Systems of Polynomial Equations and the Nullstellensatz, 2007.

[2] Jacek Bochnak, Michel Coste, Marie-Françoise Roy, Real Algebraic Geometry, Springer, 1998.

### Feasibility via quantifier elimination II

August 29, 2011

Suppose we want to decide whether the following semialgebraic set

$S := \{(x,y) \in \mathbb{R}^2 \mid x - y^2 + 1 \geq 0 \land -x - y^2 + 1 \geq 0\}$

is nonempty. This is the same as determining whether the following system of polynomial inequalities

$\begin{array}{rrl} g_1 (x, y) :=& x - y^2 + 1 &\geq 0\\ g_2 (x, y) :=& -x - y^2 +1 &\geq 0\end{array}$

is feasible. We now define $S_1 := \{(x,y) \in \mathbb{R}^2 \mid g_1 (x, y) \geq 0\}$, and $S_2 := \{(x,y) \in \mathbb{R}^2 \mid g_2 (x, y) \geq 0\}$. Set $S_1$ is depicted below

and set $S_2$ is depicted below

Note that $S = S_1 \cap S_2$. We depict this intersection below

Visual inspection of the plot of the intersection $S = S_1 \cap S_2$ allows us to conclude that $S$ in nonempty. Asserting that $S$ is nonempty is the same as stating that the proposition

$\exists{x}\exists{y} \left(g_1 (x,y) \geq 0 \land g_2 (x,y) \geq 0\right)$

is true. We can determine the truth value of this proposition via quantifier elimination using the following REDUCE + REDLOG script:

% feasibility via quantifier elimination

load_package redlog;
rlset ofsf;

% define polynomial functions
g1 :=  x-y^2+1;
g2 := -x-y^2+1;

% define existential formula
phi := ex({x,y}, g1>=0 and g2>=0);

% perform quantifier elimination
rlqe phi;

end;

which produces the truth value true. This means that there exists a $(x,y)$ that satisfies the polynomial inequalities, i.e., $S$ is nonempty.

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Remark: the plots in this post were generated by Wolfram Alpha.

### Feasibility via quantifier elimination

August 27, 2011

Consider the system of polynomial equations and inequalities [1]

$\begin{array}{rrl} f_1 (x_1, x_2) :=& x_1^2 + x_2^2 - 1 &= 0\\ g_1 (x_1, x_2) :=& 3 x_2 - x_1^3 - 2 &\geq 0\\ g_2 (x_1, x_2) :=& x_1 - 8 x_2^3 &\geq 0\end{array}$

whose solution set is the following semialgebraic set

$S := \{ (x_1,x_2) \in \mathbb{R}^2 \mid f_1 (x_1,x_2) = 0 \land g_1 (x_1,x_2) \geq 0 \land g_2 (x_1,x_2) \geq 0\}$

Parrilo showed in [1] that this system is infeasible, i.e., set $S$ is empty, using the Positivstellensatz [2]. Alternatively, one could use quantifier elimination to conclude infeasibility.

Consider the following proposition

$\exists{x_1}\exists{x_2} \left(f_1 (x_1,x_2) = 0 \land g_1 (x_1,x_2) \geq 0 \land g_2 (x_1,x_2) \geq 0\right)$.

If this proposition is true, then the system is feasible and $S$ is nonempty. If the proposition is false, then the system is infeasible and $S$ is empty. We can determine the truth value of the proposition using REDUCE + REDLOG to perform quantifier elimination:

% feasibility via quantifier elimination

load_package redlog;
rlset ofsf;

% define polynomial functions
f1 := x1^2+x2^2-1;
g1 := 3*x2-x1^3-2;
g2 := x1-8*x2^3;

% define existential formula
phi := ex({x1,x2}, f1=0 and g1>=0 and g2>=0);

% perform quantifier elimination
rlqe phi;

end;

The output of this script is the following:

The proposition is false and, thus, the system is infeasible.

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References

[1] Pablo Parrilo, Sum of Squares Programs and Polynomial Inequalities.

[2] Jacek Bochnak, Michel Coste, Marie-Françoise Roy, Real Algebraic Geometry, Springer, 1998.

### LMI representation of convex polytopes

August 7, 2011

Suppose we are given a convex polytope $P \subset \mathbb{R}^n$ defined by the intersection of $m$ closed half-spaces

$P := \{ x \in \mathbb{R}^n \mid A x \leq b \}$

where $A \in \mathbb{R}^{m \times n}$ and $b \in \mathbb{R}^m$. Note that set $P$ is the solution set of a system of $m$ non-strict linear inequalities in $n$ variables. Let

$p_i (x) := b_i - a_{i1} x_1 - a_{i2} x_2 - \dots - a_{in} x_n$

so that set $P$ can be described as follows

$P = \{ x \in \mathbb{R}^n \mid p_1 (x) \geq 0 \land p_2 (x) \geq 0 \land \dots \land p_m (x) \geq 0\}$

which allows us to conclude that $P$ is a semialgebraic set [1]. The conjunction of the $m$ non-strict linear inequalities $p_i (x) \geq 0$ is equivalent to the following linear matrix inequality (LMI) [2]

$F (x) := \left[\begin{array}{cccc} p_1 (x) & 0 & \ldots & 0\\ 0 & p_2 (x) & \ldots & 0\\ \vdots & \vdots & \ddots & \vdots\\ 0 & 0 & \ldots & p_m (x)\end{array}\right] \succeq 0$

which, since the $p_i$ polynomials are of degree 1, can be written as

$F (x) = F_0 + x_1 F_1 + x_2 F_2 + \dots + x_n F_n \succeq 0$

where the real diagonal $m \times m$ matrices $F_0, F_1, \dots, F_n$ are given by

$F_0 := \left[\begin{array}{cccc} b_1 & 0 & \ldots & 0\\ 0 & b_2 & \ldots & 0\\ \vdots & \vdots & \ddots & \vdots\\ 0 & 0 & \ldots & b_m\end{array}\right]$,    $F_j = \left[\begin{array}{cccc} -a_{1j} & 0 & \ldots & 0\\ 0 & -a_{2j} & \ldots & 0\\ \vdots & \vdots & \ddots & \vdots\\ 0 & 0 & \ldots & -a_{m j}\end{array}\right]$

for $j = 1, 2,\dots, n$. Thus, convex polytope $P$ can be defined using the LMI $F (x) \succeq 0$, as follows

$P = \{ x \in \mathbb{R}^n \mid F (x) \succeq 0 \}$

where the $F_0, F_1, \dots, F_n$ matrices are diagonal.

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References

[1] Jacek Bochnak, Michel Coste, Marie-Françoise Roy, Real Algebraic Geometry, Springer, 1998.

[2] Stephen Boyd, Laurent El Ghaoui, Eric Feron, and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory, SIAM, 1994.