# Duality

Conic duality is the starting point for MOI's duality conventions. When all functions are affine (or coordinate projections), and all constraint sets are closed convex cones, the model may be called a conic optimization problem.

For a minimization problem in geometric conic form, the primal is:

\begin{align} & \min_{x \in \mathbb{R}^n} & a_0^T x + b_0 \\ & \;\;\text{s.t.} & A_i x + b_i & \in \mathcal{C}_i & i = 1 \ldots m \end{align}

and the dual is a maximization problem in standard conic form:

\begin{align} & \max_{y_1, \ldots, y_m} & -\sum_{i=1}^m b_i^T y_i + b_0 \\ & \;\;\text{s.t.} & a_0 - \sum_{i=1}^m A_i^T y_i & = 0 \\ & & y_i & \in \mathcal{C}_i^* & i = 1 \ldots m \end{align}

where each $\mathcal{C}_i$ is a closed convex cone and $\mathcal{C}_i^*$ is its dual cone.

For a maximization problem in geometric conic form, the primal is:

\begin{align} & \max_{x \in \mathbb{R}^n} & a_0^T x + b_0 \\ & \;\;\text{s.t.} & A_i x + b_i & \in \mathcal{C}_i & i = 1 \ldots m \end{align}

and the dual is a minimization problem in standard conic form:

\begin{align} & \min_{y_1, \ldots, y_m} & \sum_{i=1}^m b_i^T y_i + b_0 \\ & \;\;\text{s.t.} & a_0 + \sum_{i=1}^m A_i^T y_i & = 0 \\ & & y_i & \in \mathcal{C}_i^* & i = 1 \ldots m \end{align}

A linear inequality constraint $a^T x + b \ge c$ should be interpreted as $a^T x + b - c \in \mathbb{R}_+$, and similarly $a^T x + b \le c$ should be interpreted as $a^T x + b - c \in \mathbb{R}_-$. Variable-wise constraints should be interpreted as affine constraints with the appropriate identity mapping in place of $A_i$.

For the special case of minimization LPs, the MOI primal form can be stated as:

\begin{align} & \min_{x \in \mathbb{R}^n} & a_0^T x &+ b_0 \\ & \;\;\text{s.t.} &A_1 x & \ge b_1\\ && A_2 x & \le b_2\\ && A_3 x & = b_3 \end{align}

By applying the stated transformations to conic form, taking the dual, and transforming back into linear inequality form, one obtains the following dual:

\begin{align} & \max_{y_1,y_2,y_3} & b_1^Ty_1 + b_2^Ty_2 + b_3^Ty_3 &+ b_0 \\ & \;\;\text{s.t.} &A_1^Ty_1 + A_2^Ty_2 + A_3^Ty_3 & = a_0\\ && y_1 &\ge 0\\ && y_2 &\le 0 \end{align}

For maximization LPs, the MOI primal form can be stated as:

\begin{align} & \max_{x \in \mathbb{R}^n} & a_0^T x &+ b_0 \\ & \;\;\text{s.t.} &A_1 x & \ge b_1\\ && A_2 x & \le b_2\\ && A_3 x & = b_3 \end{align}

and similarly, the dual is:

\begin{align} & \min_{y_1,y_2,y_3} & -b_1^Ty_1 - b_2^Ty_2 - b_3^Ty_3 &+ b_0 \\ & \;\;\text{s.t.} &A_1^Ty_1 + A_2^Ty_2 + A_3^Ty_3 & = -a_0\\ && y_1 &\ge 0\\ && y_2 &\le 0 \end{align}
Warning

For the LP case, the signs of the feasible dual variables depend only on the sense of the corresponding primal inequality and not on the objective sense.

## Duality and scalar product

The scalar product is different from the canonical one for the sets PositiveSemidefiniteConeTriangle, LogDetConeTriangle, RootDetConeTriangle.

If the set $C_i$ of the section Duality is one of these three cones, then the rows of the matrix $A_i$ corresponding to off-diagonal entries are twice the value of the coefficients field in the VectorAffineFunction for the corresponding rows. See PositiveSemidefiniteConeTriangle for details.

## Dual for problems with quadratic functions

Given a problem with quadratic functions:

\begin{align*} & \min_{x \in \mathbb{R}^n} & \frac{1}{2}x^TQ_0x + a_0^T x + b_0 \\ & \;\;\text{s.t.} & \frac{1}{2}x^TQ_ix + a_i^T x + b_i & \in \mathcal{C}_i & i = 1 \ldots m \end{align*}

with cones $\mathcal{C}_i \subseteq \mathbb{R}$ for $i = 1 \ldots m$, consider the Lagrangian function

$$$L(x, y) = \frac{1}{2}x^TQ_0x + a_0^T x + b_0 - \sum_{i = 1}^m y_i (\frac{1}{2}x^TQ_ix + a_i^T x + b_i)$$$

A pair of primal-dual variables $(x^\star, y^\star)$ is optimal if

• $x^\star$ is a minimizer of$$$\min_{x \in \mathbb{R}^n} L(x, y^\star).$$$That is,$$$0 = \nabla_x L(x, y^\star) = Q_0x + a_0 - \sum_{i = 1}^m y_i^\star (Q_ix + a_i).$$$
• and $y^\star$ is a maximizer of$$$\max_{y_i \in \mathcal{C}_i^*} L(x^\star, y).$$$That is, for all $i = 1, \ldots, m$, $\frac{1}{2}x^TQ_ix + a_i^T x + b_i$ is either zero or in the normal cone of $\mathcal{C}_i^*$ at $y^\star$. For instance, if $\mathcal{C}_i$ is $\{ x \in \mathbb{R} : x \le 0 \}$, this means that if $\frac{1}{2}x^TQ_ix + a_i^T x + b_i$ is nonzero at $x^\star$ then $y_i^\star = 0$. This is the classical complementary slackness condition.

If $\mathcal{C}_i$ is a vector set, the discussion remains valid with $y_i(\frac{1}{2}x^TQ_ix + a_i^T x + b_i)$ replaced with the scalar product between $y_i$ and the vector of scalar-valued quadratic functions.

Note

For quadratic programs with only affine constraints, the optimality condition $\nabla_x L(x, y^\star) = 0$ can be simplified as follows:

$$$0 = \nabla_x L(x, y^\star) = Q_0x + a_0 - \sum_{i = 1}^m y_i^\star a_i$$$

which gives

$$$Q_0x = \sum_{i = 1}^m y_i^\star a_i - a_0 .$$$

The Lagrangian function

$$$L(x, y) = \frac{1}{2}x^TQ_0x + a_0^T x + b_0 - \sum_{i = 1}^m y_i (a_i^T x + b_i)$$$

can be rewritten as

$$$L(x, y) = \frac{1}{2}x^TQ_0x - (\sum_{i = 1}^m y_i a_i^T - a_0^T) x + b_0 - \sum_{i = 1}^m y_i (a_i^T x + b_i)$$$

which, using the optimality condition $\nabla_x L(x, y^\star) = 0$, can be simplified as

$$$L(x, y) = -\frac{1}{2}x^TQ_0x + b_0 - \sum_{i = 1}^m y_i (a_i^T x + b_i)$$$