Sequential Linear-Quadratic Programming

Sequential linear-quadratic programming (SLQP) is an iterative method for nonlinear optimization problems where objective function and constraints are twice continuously differentiable. Similarly to sequential quadratic programming (SQP), SLQP proceeds by solving a sequence of optimization subproblems. The difference between the two approaches is that:

in SQP, each subproblem is a quadratic program, with a quadratic model of the objective subject to a linearization of the constraints
in SLQP, two subproblems are solved at each step: a linear program (LP) used to determine an active set, followed by an equality-constrained quadratic program (EQP) used to compute the total step

This decomposition makes SLQP suitable to large-scale optimization problems, for which efficient LP and EQP solvers are available, these problems being easier to scale than full-fledged quadratic programs.

It may be considered related to, but distinct from, quasi-Newton methods.

Algorithm basics

Consider a nonlinear programming problem of the form:

[math]\displaystyle{ \begin{array}{rl} \min\limits_{x} & f(x) \\ \mbox{s.t.} & b(x) \ge 0 \\ & c(x) = 0. \end{array} }[/math]

The Lagrangian for this problem is^[1]

[math]\displaystyle{ \mathcal{L}(x,\lambda,\sigma) = f(x) - \lambda^T b(x) - \sigma^T c(x), }[/math]

where [math]\displaystyle{ \lambda \ge 0 }[/math] and [math]\displaystyle{ \sigma }[/math] are Lagrange multipliers.

LP phase

In the LP phase of SLQP, the following linear program is solved:

[math]\displaystyle{ \begin{array}{rl} \min\limits_{d} & f(x_k) + \nabla f(x_k)^Td\\ \mathrm{s.t.} & b(x_k) + \nabla b(x_k)^Td \ge 0 \\ & c(x_k) + \nabla c(x_k)^T d = 0. \end{array} }[/math]

Let [math]\displaystyle{ {\cal A}_k }[/math] denote the active set at the optimum [math]\displaystyle{ d^*_{\text{LP}} }[/math] of this problem, that is to say, the set of constraints that are equal to zero at [math]\displaystyle{ d^*_{\text{LP}} }[/math]. Denote by [math]\displaystyle{ b_{{\cal A}_k} }[/math] and [math]\displaystyle{ c_{{\cal A}_k} }[/math] the sub-vectors of [math]\displaystyle{ b }[/math] and [math]\displaystyle{ c }[/math] corresponding to elements of [math]\displaystyle{ {\cal A}_k }[/math].

EQP phase

In the EQP phase of SLQP, the search direction [math]\displaystyle{ d_k }[/math] of the step is obtained by solving the following equality-constrained quadratic program:

[math]\displaystyle{ \begin{array}{rl} \min\limits_{d} & f(x_k) + \nabla f(x_k)^Td + \tfrac{1}{2} d^T \nabla_{xx}^2 \mathcal{L}(x_k,\lambda_k,\sigma_k) d\\ \mathrm{s.t.} & b_{{\cal A}_k}(x_k) + \nabla b_{{\cal A}_k}(x_k)^Td = 0 \\ & c_{{\cal A}_k}(x_k) + \nabla c_{{\cal A}_k}(x_k)^T d = 0. \end{array} }[/math]

Note that the term [math]\displaystyle{ f(x_k) }[/math] in the objective functions above may be left out for the minimization problems, since it is constant.

Notes

↑ Jorge Nocedal and Stephen J. Wright (2006). Numerical Optimization. Springer.. ISBN 0-387-30303-0. http://www.ece.northwestern.edu/~nocedal/book/num-opt.html.

References

Jorge Nocedal and Stephen J. Wright (2006). Numerical Optimization. Springer.. ISBN 0-387-30303-0. http://www.ece.northwestern.edu/~nocedal/book/num-opt.html.

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[1] Jorge Nocedal and Stephen J. Wright (2006). Numerical Optimization. Springer.. ISBN 0-387-30303-0. http://www.ece.northwestern.edu/~nocedal/book/num-opt.html.