Complex Optimization Toolbox

The Complex Optimization Toolbox is a MATLAB toolbox for optimizing problems in complex variables, although real optimization is also possible and is without performance penalty. Included are generalized algorithms for unconstrained nonlinear optimization: nonlinear conjugate gradient and limited-memory BFGS with Moré–Thuente line search or dogleg trust region, nonlinear least squares: minimization of vector-, matrix- or tensor-valued residual functions, complex bound constraints, Levenberg–Marquardt and Gauss–Newton with CG–Steihaug or dogleg trust region, and much more: automated numerical real and complex differentiation, preservation of unknowns in their original format (i.e., as a vector, matrix, tensor or even a cell array of tensors), preconditioned conjugate gradient, … The Complex Optimization Toolbox is part of Tensorlab, a MATLAB toolbox for tensor computations. Please consult the Tensorlab user guide to get started with the Complex Optimization Toolbox. Alternatively, see the toolbox’s Contents.m for an overview of its functionality. For questions, bug reports or other inquiries, please contact cot@cs.kuleuven.be.