This paper considers the constrained convex optimization problem with the least constraint violation under a general measure function. The properties of the conjugate dual associated with the measure function of the shifted problem are discussed through the relations between the dual function and the ...
convex optimization with non-linear constraint. Learn more about optimization, non-linear, convex, constraint, objective
A general constraint aggregation technique is proposed for convex optimization problems. At each iteration a set of convex inequalities and linear equations is replaced by a single surrogate inequality formed as a linear combination of the original constraints. After solving the simplified subproblem, new...
Convex optimization (CO) is where the objective and all constraint functions are convex. From: Journal of Building Engineering, 2020 About this pageSet alert Also in subject area: MathematicsDiscover other topics On this page Definition Chapters and Articles Related Terms Recommended Publications Chapter...
However, depending on the system models and the considered constraints, many optimization problems in MU-MIMO systems can be solved in non-convex or non-polynomial time. Moreover, for the enormous complexity of the resource allocation problem, the original problem must be loosened, non-convex ...
New Constraint Qualification and Conjugate Duality for Composed Convex Optimization Problemsdoi:10.1007/s10957-007-9247-4Conjugate functions - Fenchel-Lagrange duality - Composed convex optimization problems - Cone constraint qualifications...
We propose a new approach to convex nonlinear multiobjective optimization that captures the geometry of the Pareto set by generating a discrete set of Pareto points optimally. We show that the problem of nding a maximally uniform representation of the Pareto surface can be formulated as a mathemat...
Optimization Letters Zhou Wei& Jen-Chih Yao 262Accesses 2Citations 1Altmetric Explore all metrics Abstract In this paper, we study constraint qualifications for the nonconvex inequality defined by a proper lower semicontinuous function. These constraint qualifications involve the generalized construction of...
He, M.: Infeasible constraint reduction for linear and convex quadratic optimization. Ph.D. thesis, University of Maryland (2011). http://hdl.handle.net/1903/12772. Accessed 2019 He, M.Y., Tits, A.L.: Infeasible constraint-reduced interior-point methods for linear optimization. Optim. Me...
This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this ...