Convexification procedures and decomposition methods for nonconvex optimization problems
نویسندگان
چکیده
منابع مشابه
Convexification Procedures and Decomposition Methods for Nonconvex Optimization Problems 1
In order for primal-dual methods to be applicable to a constrained minimization problem, it is necessary that restrictive convexity conditions are satisfied. In this paper, we consider a procedure by means of which a nonconvex problem is convexified and transformed into one which can be solved with the aid of primal-dual methods. Under this transformation, separability of the type necessary for...
متن کاملComplete decomposition algorithm for nonconvex separable optimization problems and applications
Abstraet~In this paper, we present a complete decomposition algorithm for nonconvex separable optimization problems applied in the optimal control problems. This complete decomposition algorithm combines recursive quadratic programming with the dual method. When our algorithm is applied to discretized optimal control problems, a simple and parallel computation and a simple and regular data flow...
متن کاملRecursive Decomposition for Nonconvex Optimization
Continuous optimization is an important problem in many areas of AI, including vision, robotics, probabilistic inference, and machine learning. Unfortunately, most real-world optimization problems are nonconvex, causing standard convex techniques to find only local optima, even with extensions like random restarts and simulated annealing. We observe that, in many cases, the local modes of the o...
متن کاملQuasi-Newton Methods for Nonconvex Constrained Multiobjective Optimization
Here, a quasi-Newton algorithm for constrained multiobjective optimization is proposed. Under suitable assumptions, global convergence of the algorithm is established.
متن کاملConvex Relaxation Methods for Nonconvex Polynomial Optimization Problems
This paper introduces to constructing problems of convex relaxations for nonconvex polynomial optimization problems. Branch-and-bound algorithms are convex relaxation based. The convex envelopes are of primary importance since they represent the uniformly best convex underestimators for nonconvex polynomials over some region. The reformulationlinearization technique (RLT) generates LP (linear p...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Optimization Theory and Applications
سال: 1979
ISSN: 0022-3239,1573-2878
DOI: 10.1007/bf00937167