- Referenced in 206 articles
- primal-dual interior-point method for general nonlinear programming. We focus in this paper mainly ... brief mention of the extensions to convex and general nonlinear programming, since a detailed paper...
- Referenced in 112 articles
- points) and extreme rays of a general convex polyhedron given by a system of linear ... given as the Minkowski sum of the convex hull of a finite set of points ... these basic functions, cdd/cdd+ can solve the general linear programming (LP) problem to maximize...
- Referenced in 105 articles
- convex nonlinear and semidefinite programming (NLP-SDP). The algorithm used in PENNON is a generalized ... Zibulevsky for convex NLP problems. We present generalization of this algorithm to convex...
- Referenced in 31 articles
- generalized version of the pool-adjacent-violators algorithm (PAVA) to minimize a separable convex function ... with simple chain constraints. Besides of general convex functions we extend existing PAVA implementations...
- Referenced in 76 articles
- that computes the convex hull of a point set in general (but small!) dimension ... list of facets of the convex hull of the points, each facet presented...
- Referenced in 16 articles
- package alphahull: Generalization of the convex hull of a sample of points in the plane ... concepts of alpha-shape and alpha-convex hull generalize the definition of the convex hull...
- Referenced in 30 articles
- worst-case to solving a convex semidefinite program, generalizing previous works on performance estimation...
- Referenced in 18 articles
- LMITOOL and YALMIP. The much newer general purpose convex optimization package CVX serves the same...
- Referenced in 11 articles
- points) and extreme rays of a general convex polyhedron in R^d given...
- Referenced in 8 articles
- called SpicyMKL, which is applicable to general convex loss functions and general types of regularization...
- Referenced in 34 articles
- convex quadratic programming are considered. A pathological example shows that the faster ... provided for general use a Fortran subroutine [”ZQPCVX: a Fortran subroutine for convex, quadratic programming ... Fletcher, ”A Fortran subroutine for general quadratic programming”, Report AERE-R 6370, Harwell ... Idnani is very suitable in practice for convex quadratic programming calculations...
- Referenced in 15 articles
- gradient sampling methods. For general unconstrained minimization: convex or nonconvex, smooth or nonsmooth, including BFGS...
- Referenced in 4 articles
- Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior ... quadratically constrained quadratic programming problems, and general convex programming problems in fewer iterations. Moreover, several ... iOptimize detects infeasibility more reliably than the general nonlinear solvers Ipopt (version 3.9.2) and Knitro...
- Referenced in 45 articles
- general MINLP problems and global optimal solutions can be ensured for pseudo-convex MINLP problems...
- Referenced in 6 articles
- open source software for general convex optimization. It assumes that a user-made code, thereafter...
- Referenced in 9 articles
- algorithm is given both for uniformly convex and general nonlinear functions under the exact...
- Referenced in 74 articles
- projected gradient algorithm for solving large-scale convex-constrained optimization problems. It combines the classical ... substantially more efficient than existing general-purpose software on problems for which projections...
- Referenced in 154 articles
- computes the defining hyperplanes, and vice versa), convex hulls, a triangulation of a vector ... grading (for example, for rational polytopes), generalized (or weighted) Ehrhart series and Lebesgue integrals...
- Referenced in 28 articles
- smoothing techniques generally have a difficult time accommodating qualitative constraints like monotonicity, convexity or boundary ... optimal rate of convergence. Several options and generalizations are included in COBS: it can handle...
- Referenced in 45 articles
- Then, libMC calculates enclosures as well as convex and concave relaxations recursively for each ... relaxation techniques . Because McCormick relaxations are generally non-smooth, subgradients (as opposed to gradients ... forward mode, with each elementary operation of convex and concave relaxation, additional variables are introduced...