Model Predictive Control

2.2 Lagrange Duality Theory 11
where u = [u1, . . . , um] ′ , v = [v1, . . .,vp] ′ and L : R s × R m × R p → R. The
components ui and vi are called Lagrange multipliers or dual variables. Note
that the i-th dual variable ui is associated with the i-th inequality constraint of
problem (2.1), the i-th dual variable vi is associated with the i-th equality constraint
of problem (2.1).
Let z be a feasible point: for arbitrary vectors u ≥ 0 and v we trivially obtain
a lower bound on f(z)
L(z, u, v) ≤ f(z). (2.4)
We minimize both sides of equation (2.4)
inf L(z, u, v) ≤ inf f(z) (2.5)
z∈Z, g(z)≤0, h(z)=0 z∈Z, g(z)≤0, h(z)=0
in order to reconstruct the original problem on the right-hand side of the expression.
Since for arbitrary u ≥ 0 and v
we obtain
inf L(z, u, v) ≥ inf L(z, u, v), (2.6)
z∈Z, g(z)≤0, h(z)=0 z∈Z
inf L(z, u, v) ≤ inf f(z). (2.7)
z∈Z z∈Z, g(z)≤0, h(z)=0
Equation (2.7) implies that for arbitrary u ≥ 0 and v the solution to
inf
z L(z, u, v), (2.8)
provides us with a lower bound to the original problem. The “best” lower bound
is obtained by maximizing problem (2.8) over the dual variables
sup
inf
(u,v), u≥0 z∈Z
Define the dual cost Θ(u, v) as follows
L(z, u, v) ≤ inf
z∈Z, g(z)≤0, h(z)=0 f(z).
Θ(u, v) inf L(z, u, v) ∈ [−∞, +∞]. (2.9)
z∈Z
Then the Lagrange dual problem is defined as
sup
(u,v), u≥0
Θ(u, v). (2.10)
The dual cost Θ(u, v) is the optimal value of an unconstrained optimization
problem. Problem (2.9) is called Lagrange dual subproblem. Only points (u, v)
with Θ(u, v) > −∞ are interesting for the Lagrange dual problem. A point (u, v)
will be called dual feasible if u ≥ 0 and Θ(u, v) > −∞. Θ(u, v) is always a
concave function since it is the pointwise infimum of a family of affine functions
of (u, v). This implies that the dual problem is a convex optimization problem (max
of a concave function over a convex set) even if the original problem is not convex.
Therefore, it is much easier to solve the dual problem than the primal (which is in