NEXT: In-Network Nonconvex Optimization
نویسندگان
چکیده
منابع مشابه
Nonconvex Optimization Is Combinatorial Optimization
Difficult nonconvex optimization problems contain a combinatorial number of local optima, making them extremely challenging for modern solvers. We present a novel nonconvex optimization algorithm that explicitly finds and exploits local structure in the objective function in order to decompose it into subproblems, exponentially reducing the size of the search space. Our algorithm’s use of decom...
متن کاملNonconvex Robust Optimization
We propose a novel robust optimization technique, which is applicable to nonconvex and simulation-based problems. Robust optimization finds decisions with the best worst-case performance under uncertainty. If constraints are present, decisions should also be feasible under perturbations. In the real-world, many problems are nonconvex and involve computer-based simulations. In these applications...
متن کاملDuality Theory: Biduality in Nonconvex Optimization Duality Theory: Biduality in Nonconvex Optimization
It is known that in convex optimization, the Lagrangian associated with a constrained problem is usually a saddle function, which leads to the classical saddle Lagrange duality (i. e. the monoduality) theory. In nonconvex optimization, a so-called superLagrangian was introduced in [1], which leads to a nice biduality theory in convex Hamiltonian systems and in the so-called d.c. programming.
متن کاملNonconvex Optimization for Communication Systems
Convex optimization has provided both a powerful tool and an intriguing mentality to the analysis and design of communication systems over the last few years. A main challenge today is on nonconvex problems in these application. This paper presents an overview of some of the important nonconvex optimization problems in point-to-point and networked communication systems. Three typical applicatio...
متن کاملConvex Optimization with Nonconvex Oracles
In machine learning and optimization, one often wants to minimize a convex objective function F but can only evaluate a noisy approximation F̂ to it. Even though F is convex, the noise may render F̂ nonconvex, making the task of minimizing F intractable in general. As a consequence, several works in theoretical computer science, machine learning and optimization have focused on coming up with pol...
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: IEEE Transactions on Signal and Information Processing over Networks
سال: 2016
ISSN: 2373-776X,2373-7778
DOI: 10.1109/tsipn.2016.2524588