Cone Programming on CVXOPT in Python

In a previous post of mine, I talked about CVXOPT Programming. In this one, we’ll introduce cone programming. So first things first. Cone programming is a term (short for second-order cone program (SOCP)) is a convex optimization problem of the following form

(1) $\begin{equation*}\begin{aligned}& \underset{x}{\text{minimize}}& & f^T x \\& \text{subject to}& & \lVert A_i x + b_i \rVert_2 \leq c_i^T x + d_i,\quad i = 1,\dots,m \\& & & Fx = g\end{aligned}\end{equation*}$

where matrices $A_i$ fall in $\mathbb{R}^{n_i \times n}$ and vectors $b_i \in \mathbb{R}^{n_i \times 1}$ . On the other hand, vectors $c_i \in \mathbb{R}^{n \times 1}$ and $f \in \mathbb{R}^{n \times 1}$ . Also $d_i \in \mathbb{R}$ . Finally, $F \in \mathbb{R}^{p \times n }$ and $g \in \mathbb{R}^{p \times 1}$ .

SOCPs are easily solved on CVXOPT using the socp solver found in the solvers module of CVXOPT. Please refer to the video for a step-by-step tutorial.

On the other hand, Quadratically Constrained Quadratic Programs (a.k.a QCQPs) are a bit tricky. QCQPs are optimization problems in which both the objective function and the constraints are quadratic functions, i.e.

(2) $\begin{equation*}\begin{aligned}& \underset{x}{\text{minimize}}& & x^\mathrm{T} P_0 x + q_0^\mathrm{T} x \\& \text{subject to}& & x^\mathrm{T} P_i x + q_i^\mathrm{T} x + r_i \leq 0,\quad i = 1,\dots,m \\& & & Ax = b\end{aligned}\end{equation*}$

where all matrices $P_0 \ldots P_m$ are given matrices. The QCQP problem is convex if matrices $P_0 \ldots P_m$ are positive semi-definite. Note that a QCQP boils down to a QP (Quadratic Program) when matrices $P_1 \ldots P_m$ are zero matrices. If in addition to the aforementioned statements, we also have $P_0 = 0$ , then the problem is an LP (Linear Program).

That’s enough background to get you going with QCQPs. Turns out that CVXOPT does not have a “QCQP” solver. On the bright side, we can cast a QCQP as an SOCP enabling us to solve QCQPs via the SOCP solver of CVXOPT. How do we do that ? Well minimizing the cost of the QCQP, i.e. minimizing $\tfrac12 x^\mathrm{T} P_0 x + q_0^\mathrm{T} x$ is the same as minimizing an upper bound of $\tfrac12 x^\mathrm{T} P_0 x + q_0^\mathrm{T} x$ as

(3) $\begin{equation*}\begin{aligned}& \underset{x,\gamma}{\text{minimize}}& & \gamma \\& \text{subject to}& & x^\mathrm{T} P_i x + q_i^\mathrm{T} x + r_i \leq 0,\quad i = 1,\dots,m \\& & & Ax = b \\& & & x^\mathrm{T} P_0 x + q_0^\mathrm{T} x \leq \gamma \end{aligned}\end{equation*}$

We will now make use of an important theorem of positive semi-definite matrices, that is

Theorem 1: Let $A$ be a positive semidefinite matrix in $\mathbb{C}^{n \times n}$ . Then there is exactly one positive semidefinite matrix $B$ such that $A = B^H B$ .

Actually, the above theorem is very intuitive. It’s a generalization of something you learned back in high school. It’s a matrix way of saying that every non-negative number $a$ admits a square root $b = \sqrt{a}$ . So making use of the matrix square root decomposition, we will write down the square root decompositions of all matrices $P_0 \ldots P_m$ as

(4) $\begin{equation*}P_i = A_i^T A_i, \qquad i = 1 \ldots m\end{equation*}$

We get

(5) $\begin{equation*}\begin{aligned}& \underset{x,\gamma}{\text{minimize}}& & \gamma \\& \text{subject to}& & x^\mathrm{T} A_i^T A_i x + q_i^\mathrm{T} x + r_i \leq 0,\quad i = 1,\dots,m \\& & & Ax = b \\& & & x^\mathrm{T} A^T A x + q_0^\mathrm{T} x \leq \gamma\end{aligned}\end{equation*}$

We shall also decompose $q_i$ ‘s as

(6) $\begin{equation*}q_i = 2 A_i^T b_i, \qquad i = 0 \ldots m\end{equation*}$

So we get

(7) $\begin{equation*}\begin{aligned}& \underset{x,\gamma}{\text{minimize}}& & \gamma \\& \text{subject to}& & x^\mathrm{T} A_i^T A_i x + 2x^T A_i^T b_i + r_i \leq 0,\quad i = 1,\dots,m \\& & & Ax = b \\& & & x^\mathrm{T} A_0^T A_0 x + 2 x^T A_0^T b_0 \leq \gamma\end{aligned}\end{equation*}$

Now let us include the term $b_i^T b_i$ in the first $m+1$ constraints as

(8) $\begin{equation*}\begin{aligned}& \underset{x,\gamma}{\text{minimize}}& & \gamma \\& \text{subject to}& & x^\mathrm{T} A_i^T A_i x + 2x^T A_i^T b_i +b_i^T b_i - b_i^T b_i +r_i \leq 0,\quad i = 1,\dots,m \\& & & Ax = b \\& & & x^\mathrm{T} A_0^T A_0 x + 2 x^T A_0^T b_0 \leq \gamma\end{aligned}\end{equation*}$

Similarly, we include the term $b^T b$ in the last constraint as

(9) $\begin{equation*}\begin{aligned}& \underset{x,\gamma}{\text{minimize}}& & \gamma \\& \text{subject to}& & x^\mathrm{T} A_i^T A_i x + 2x^T A_i^T b_i +b_i^T b_i - b_i^T b_i +r_i \leq 0,\quad i = 1,\dots,m \\& & & Ax = b \\& & & x^\mathrm{T} A_0^T A_0 x + 2 x^T A_0^T b_0 +b^T b_0 - b_0^T b_0 \leq \gamma\end{aligned}\end{equation*}$

Why did we do that ? Well, notice that

(10) $\begin{equation*}\Vert Ax + b \Vert^2 = x^T A^T A x + 2x^T A^T b +b^T b\end{equation*}$

This means that

(11) $\begin{equation*}\begin{aligned}& \underset{x,\gamma}{\text{minimize}}& & \gamma \\& \text{subject to}& & \Vert A_ix + b_i \Vert^2 \leq b_i^T b_i - r_i ,\quad i = 1,\dots,m \\& & & Ax = b \\& & & \Vert A_0x + b_0 \Vert^2 \leq b_0^T b_0 + \gamma\end{aligned}\end{equation*}$

The problem in equation (11) is really close to that of (1). The only thing that is a bit “annoying” is the $\gamma$ . To bypass this problem, we bound $\gamma$ by a user-defined parameter as $\gamma \leq T$

(12) $\begin{equation*}\begin{aligned}& \underset{x,\gamma}{\text{minimize}}& & \gamma \\& \text{subject to}& & \Vert A_ix + b_i \Vert^2 \leq b_i^T b_i - r_i ,\quad i = 1,\dots,m \\& & & Ax = b \\& & & \Vert A_0x + b_0 \Vert^2 \leq b_0^T b_0 + T \\& & & \gamma \leq T\end{aligned}\end{equation*}$

The above problem is an SOCP as

(13) $\begin{equation*}\begin{aligned}& \underset{\bar{x}}{\text{minimize}}& & f^T \bar{x} \\& \text{subject to}& & \lVert \bar{A}_i \bar{x} + b_i \rVert_2 \leq c_i^T \bar{x} + d_i,\quad i = 0,\dots,m+1 \\& & & F\bar{x} = g\end{aligned}\end{equation*}$

where

(14) $\begin{align*}\bar{x} &=\begin{bmatrix}\gamma & x\end{bmatrix}^T \\f &= \begin{bmatrix}1 & 0 \ldots 0\end{bmatrix}^T \\\bar{A}_i &=\begin{bmatrix}\mathbf{0} & A_i\end{bmatrix}, \qquad i = 0 \ldots m \\\bar{A}_{m+1} &= \begin{bmatrix}1 & 0 & \ldots & 0 \\0 & 0 & \ldots & 0 \\\vdots &\vdots & \vdots & \vdots \\0 & 0 & \ldots & 0 \end{bmatrix} \\b_{m+1} &= \mathbf{0} \\c_i &= \mathbf{0}, \qquad i = 0 \ldots m+1 \\d_0 &= b_0^T b_0 + T \\ d_i &= b_i^Tb_i - r_i , \qquad i = 1 \ldots m \\d_{m+1} &= T^2 \\F &= A \\g &= b\end{align*}$

So, in short, by choosing the above variables, we can go from a QCQP to an SOCP and hence solve the former on CVXOPT. The lecture uses Jupyter notebook on my favorite Google Chrome browser.

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Cone Programming on CVXOPT in Python

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