CUTE: constrained and unconstrained testing environment
ACM Transactions on Mathematical Software (TOMS)
Primal-dual interior-point methods
Primal-dual interior-point methods
An SQP method for general nonlinear programs using only equality constrained subproblems
Mathematical Programming: Series A and B
Global convergence analysis of algorithms for finding feasible points in norm-relaxed MFD
Journal of Optimization Theory and Applications
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
A Computationally Efficient Feasible Sequential Quadratic Programming Algorithm
SIAM Journal on Optimization
The Constraint Consensus Method for Finding Approximately Feasible Points in Nonlinear Programs
INFORMS Journal on Computing
A Multidimensional Filter Algorithm for Nonlinear Equations and Nonlinear Least-Squares
SIAM Journal on Optimization
Improved constraint consensus methods for seeking feasibility in nonlinear programs
Computational Optimization and Applications
Differential evolution with multi-constraint consensus methods for constrained optimization
Journal of Global Optimization
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Whether a given nonlinear solver can reach a feasible point for a set of nonlinear constraints depends heavily on the initial point provided. We develop a range of computationally cheap constraint consensus algorithms that move from a given initial point to a better final point that is then passed to the nonlinear solver. Empirical tests show that this added step greatly improves the success rate of various nonlinear solvers in reaching feasibility, and reduces the effort they expend in doing so. We also develop a new initial point placement heuristic for use when an initial point is not provided by the modeller. Empirical tests show much improved performance for this new heuristic, both alone and in conjunction with the constraint consensus algorithms.