Sequential quadratic programming

Sequential quadratic programming (SQP) is an iterative method for constrained nonlinear optimization which may be considered a quasi-Newton method. SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable, but not necessarily convex.

SQP methods solve a sequence of optimization subproblems, each of which optimizes a quadratic model of the objective subject to a linearization of the constraints. If the problem is unconstrained, then the method reduces to Newton's method for finding a point where the gradient of the objective vanishes. If the problem has only equality constraints, then the method is equivalent to applying Newton's method to the first-order optimality conditions, or Karush–Kuhn–Tucker conditions, of the problem.

Algorithm basics

Overview schematic illustrating the basic SQP algorithm

Consider a nonlinear programming problem of the form:

${\begin{array}{rl}\min \limits _{x}&f(x)\\{\mbox{subject to}}&h(x)\geq 0\\&g(x)=0.\end{array}}$

The Lagrangian for this problem is

${\mathcal {L}}(x,\lambda ,\sigma )=f(x) \lambda h(x) \sigma g(x),$

where $\lambda$ and $\sigma$ are Lagrange multipliers.

The standard Newton's Method searches for the solution $\nabla {\mathcal {L}}(x,\lambda ,\sigma )=0$ by iterating the following equation, where $\nabla _{xx}^{2}$ denotes the Hessian matrix:

${\begin{bmatrix}x_{k 1}\\\lambda _{k 1}\\\sigma _{k 1}\end{bmatrix}}={\begin{bmatrix}x_{k}\\\lambda _{k}\\\sigma _{k}\end{bmatrix}}-\underbrace {{\begin{bmatrix}\nabla _{xx}^{2}{\mathcal {L}}&\nabla h&\nabla g\\\nabla h^{T}&0&0\\\nabla g^{T}&0&0\end{bmatrix}}^{-1}} _{\nabla ^{2}{\mathcal {L}}}\underbrace {\begin{bmatrix}\nabla f \lambda _{k}\nabla h \sigma _{k}\nabla g\\h\\g\end{bmatrix}} _{\nabla {\mathcal {L}}}$.

However, because the matrix $\nabla ^{2}{\mathcal {L}}$ is generally singular (and therefore non-invertible), the Newton step $d_{k}=\left(\nabla _{xx}^{2}{\mathcal {L}}\right)^{-1}\nabla {\mathcal {L}}$ cannot be calculated directly. Instead the basic sequential quadratic programming algorithm defines an appropriate search direction $d_{k}$ at an iterate $(x_{k},\lambda _{k},\sigma _{k})$, as a solution to the quadratic programming subproblem

${\begin{array}{rl}\min \limits _{d}&f(x_{k}) \nabla f(x_{k})^{T}d {\tfrac {1}{2}}d^{T}\nabla _{xx}^{2}{\mathcal {L}}(x_{k},\lambda _{k},\sigma _{k})d\\\mathrm {s.t.} &h(x_{k}) \nabla h(x_{k})^{T}d\geq 0\\&g(x_{k}) \nabla g(x_{k})^{T}d=0.\end{array}}$

where the quadratic form is formed with the Hessian of the Lagrangian. Note that the term $f(x_{k})$ in the expression above may be left out for the minimization problem, since it is constant under the $\min \limits _{d}$ operator.

Together, the SQP algorithm starts by first choosing the initial iterate $(x_{0},\lambda _{0},\sigma _{0})$, then calculating $\nabla ^{2}{\mathcal {L}}(x_{0},\lambda _{0},\sigma _{0})$ and $\nabla {\mathcal {L}}(x_{0},\lambda _{0},\sigma _{0})$. Then the QP subproblem is built and solved to find the Newton step direction $d_{0}$ which is used to update the parent problem iterate using $\left[x_{k 1},\lambda _{k 1},\sigma _{k 1}\right]^{T}=\left[x_{k},\lambda _{k},\sigma _{k}\right]^{T} d_{k}$. This process is repeated for $k=0,1,2,\ldots$ until the parent problem satisfies a convergence test.

Practical implementations

Practical implementations of the SQP algorithm are significantly more complex than its basic version above. To adapt SQP for real-world applications, the following challenges must be addressed:

• The possibility of an infeasible QP subproblem.
• QP subproblem yielding a bad step: one that either fails to reduce the target or increases constraints violation.
• Breakdown of iterations due to significant deviation of the target/constraints from their quadratic/linear models.

To overcome these challenges, various strategies are typically employed:

• Use of merit functions, which assess progress towards a constrained solution, or filter methods.
• Trust region or line search methods to manage deviations between the quadratic model and the actual target.
• Special feasibility restoration phases to handle infeasible subproblems, or the use of L1-penalized subproblems to gradually decrease infeasibility

These strategies can be combined in numerous ways, resulting in a diverse range of SQP methods.

Alternative approaches

• Sequential linear programming
• Sequential linear-quadratic programming
• Augmented Lagrangian method

Implementations

SQP methods have been implemented in well known numerical environments such as MATLAB and GNU Octave. There also exist numerous software libraries, including open source:

• SciPy (de facto standard for scientific Python) has scipy.optimize.minimize(method='SLSQP') solver.
• NLopt (C/C implementation, with numerous interfaces including Julia, Python, R, MATLAB/Octave), implemented by Dieter Kraft as part of a package for optimal control, and modified by S. G. Johnson.
• ALGLIB SQP solver (C , C#, Java, Python API)

and commercial

• LabVIEW
• KNITRO (C, C , C#, Java, Python, Julia, Fortran)
• NPSOL (Fortran)
• SNOPT (Fortran)
• NLPQL (Fortran)
• MATLAB
• SuanShu (Java)

See also

• Newton's method
• Secant method
• Model Predictive Control

References

• Bonnans, J. Frédéric; Gilbert, J. Charles; Lemaréchal, Claude; Sagastizábal, Claudia A. (2006). Numerical optimization: Theoretical and practical aspects. Universitext (Second revised ed. of translation of 1997 French ed.). Berlin: Springer-Verlag. pp. xiv 490. doi:10.1007/978-3-540-35447-5. ISBN 978-3-540-35445-1. MR 2265882.
• Jorge Nocedal and Stephen J. Wright (2006). Numerical Optimization. Springer. ISBN 978-0-387-30303-1.

External links

• Sequential Quadratic Programming at NEOS guide