# Bad SDPs and beginner mistakes

Beginners in optimization and semidefinite programming often overestimate what semidefinite programming solvers are capable of, and what the L in LMI stands for. Sometimes completely unrealistically complicated models are derived, while sometimes trivial improvements can be made to allow them to be solved.

## Unnecessary nonlinear terms

The absolutely most common mistake is the introduction of a single variable nonlinearly which destroys both convexity and linearity. A too literate reading of a theorem and lack of understanding what L stands for often leads to models similiar to this

```
P = sdpvar(2);
A = [-1 2;2 -3];
B = [1;1];
sdpvar gamma
MyLMI = [P >= 0, [A'*P + P*A P*B;B'*P -gamma^2] <= 0];
optimize(MyLMI, gamma)
```

For comical effect, we have named the obviously nonlinear constraint **MyLMI** to indicate the lack of understanding here. L in LMI stands for linear, and the squared **gamma** obviously destroys that here.

When sending this model to optimize it will either lead to a diagnostic message saying there is no solver available, or you might get the global nonlinear solver BMIBNB started (and most likely fail). This is completely unnecessary though.

A completely equivalent form, as **gamma** only is used in squared form, is to introduce a new variable which replaces the squared term.

```
P = sdpvar(2);
A = [-1 2;2 -3];
B = [1;1];
sdpvar t
MyLMI = [P >= 0, [A'*P + P*A P*B;B'*P -t] <= 0];
optimize(MyLMI, sqrt(t))
```

This is still essentially as bad though, as we now try to minimize a concave objective and this cannot be reformulated as a linear semidefinite program. However, the objective is monotonic so an equivalent linear problem can be solved, and the original variable can be recovered.

```
P = sdpvar(2);
A = [-1 2;2 -3];
B = [1;1];
sdpvar t
MyLMI = [P >= 0, [A'*P + P*A P*B;B'*P -t] <= 0];
optimize(MyLMI, t)
gamma = value(t)^2
```

## A Schur complement away

The most commonly trick used in control related semidefinite programs is the Schur complement. Consider the following model were we try to find a minimum-norm state-feedback matrix to stabilize a given system with predefined Lyapunov function

```
K = sdpvar(1,2);
A = [-1 2;2 -3];
B = [1;1];
P = [33 -56;-56 97];
MyLMI = [ (A + B*K)'*P*(A + B*K) - P <= 0];
optimize(MyLMI,norm(K))
```

Once again we indicate how confused we are giving the quadratic semidefinite constraint a name indicating we think it is linear. The correct model is only a Schur complement away

```
K = sdpvar(1,2);
A = [-1 2;2 -3];
B = [1;1];
P = [33 -56;-56 97];
MyLMI = [ [P (A + B*K)';(A + B*K) inv(P)] >= 0];
optimize(MyLMI,norm(K))
```

## Congruence and variable change

An even more complicated model would arise if we wanted to optimize over both **K** and **P** in the previous example (now only looking both a feasible solution, since the minimum-norm state-feedback problem cannot be expressed using a convex semidefinite model)

The most naive model would be the following

```
K = sdpvar(1,2);
P = sdpvar(2);
A = [-1 2;2 -3];
B = [1;1];
MyLMI = [ (A + B*K)'*P*(A + B*K) - P <= 0];
optimize(MyLMI,norm(K))
```

The initial model is cubic in the decision variables. After reading the previous section, our hero might develop the following model

```
K = sdpvar(1,2);
P = sdpvar(2);
A = [-1 2;2 -3];
B = [1;1];
MyLMI = [ [P (A + B*K)';(A + B*K) inv(P)] >= 0];
optimize(MyLMI)
```

The cubic terms have been eliminated, but this model is arguably even worse, as it involves an inverse. The trick here is to employ a congruence transformation with a block-diagonal matrix with blocks \( P^{-1} \) and \( I \), or alternatively view the original constraint as \( P^{-1}(A + BK)^T P (A + BK)P^{-1} - P^{-1}PP^{-1} \preceq 0\). This leads to

```
K = sdpvar(1,2);
P = sdpvar(2);
A = [-1 2;2 -3];
B = [1;1];
MyLMI = [ [inv(P) inv(P)*(A + B*K)';(A + B*K)*inv(P) inv(P)] >= 0];
optimize(MyLMI)
```

Even worse, but now we perform an invertible variable change using \( Q = P^{-1}, Y = KP^{-1} \) and the result is linear

```
Y = sdpvar(1,2);
Q = sdpvar(2);
A = [-1 2;2 -3];
B = [1;1];
MyLMI = [ [Q (A*Q + B*Y)';(A*Q + B*Y) Q] >= 0];
optimize(MyLMI)
K = value(Y)*inv(value(Q))
```

## Impossible Schur complement

Beginners who have learned the magic of Schur complements to linearize \(A - B^TC^{-1}B \succeq, C\succeq 0\) sometimes start bending backwards to massage the nonconvex model \(A + B^TC^{-1}B \succeq, C\succeq 0\) into a convex form, for instance by writing it as \(A - B^T(-C^{-1})B \succeq, C\succeq 0\). It simply does not work as it is a nonconvex constraint. The flaw here is that we forgot that the correct form after changing the sign would be \(A - B^T(-C^{-1})B \succeq 0, -C\succeq 0\), which is very different from the initial constraint.

## That is not a variable change

Another typical mistake is to fail to understand the difference between a variable change, and simply assigning expressions to temporary variables. The state-feedback design problem above is not fixed by simply assigning the variables to some other expression. This is just a re-arrangement of code by introducing a temporary expression holder, the resulting model is precisely the same.

```
K = sdpvar(1,2);
P = sdpvar(2);
A = [-1 2;2 -3];
B = [1;1];
Q = inv(P);
Y = K*inv(P);
MyLMI = [ [Q (A*Q + B*Y)';(A*Q + B*Y) Q] >= 0];
optimize(MyLMI)
```

Similarily, the following model does indeed introduce new variables, but the old ones are unnecessarily kept and adds nonlinear equalities to the model so it can still not be solved.

```
K = sdpvar(1,2);
P = sdpvar(2);
Y = sdpvar(1,2);
Q = sdpvar(2);
A = [-1 2;2 -3];
B = [1;1];
Q = inv(P);
Y = K*inv(P);
MyLMI = [ [Q (A*Q + B*Y)';(A*Q + B*Y) Q] >= 0, Q == inv(P), Y == K*inv(P)];
optimize(MyLMI)
```

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