Variations and extension of the convex–concave procedure

By Thomas Lipp, Stephen P. Boyd. 2016

796 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/b85be4c3b00302f43e647471e5d04fdba909acb7

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In Computer Networks and Communications

In Industrial and Manufacturing Engineering

In Numerical Analysis

Citations in the corpus

A General Framework for Counterfactual Learning-to-Rank (2018, methodology): “It can be shown that this procedure converges to a local optimum [18].”
Support points (2016, background): “, [70]), such methods are typically quite slow in practice [38], and may not be appropriate for the large-scale problem at hand.”
Convex Optimization Based Distributed Optimal Gas-Power Flow Calculation (2018, methodology): “According to [30], (37) can always be satisfied given a large jmax .”
Recycling Privileged Learning and Distribution Matching for Fairness (2017, background): “In our initial experiments, solving (7) with CCP tends to ignore the fairness constraint, therefore we do not explore this approach further.”
Grid-Aware Aggregation and Realtime Disaggregation of Distributed Energy Resources in Radial Networks (2019, background): “This successive approach is related to the convex-concave procedure [29].”
Disciplined convex-concave programming (2016, methodology): “The method we present in this paper is an extension of the penalty CCP method introduced in [30], given as algorithm I.”
Reconfigurable Intelligent Surfaces: Principles and Opportunities (2020, methodology): “The formulated non-convex problem was transformed into a sequence of semidefinite programming subproblems, where the CSI uncertainties and the non-convex unit-modulus constraints are handled by applying approximation transformations and convex-concave procedure, respectively [81].”
Hopfield Networks is All You Need (2020, background): “Although the objective converges in all cases, it does not necessarily converge to a local minimum [43].”
Structured H∞‐control of infinite‐dimensional systems (2017, background): “A general analysis of CCP together with variations and extensions is discussed in [15].”
Full-Duplex Communication for ISAC: Joint Beamforming and Power Optimization (2022, background): “…respect to Ψ , and is also convex with respect to Similarly, the right-hand side of (22) is convex with respect to { V l } Ll =0 . constraint Therefore, (22) is a difference-of-convex (DC) which can be handled by iteratively lower bounding the left-hand side by its ﬁrst-order Taylor expansion [31].”
Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization (2020, methodology): “The convergence of SCA has been proven in [32], and the algorithm will stop after finite iterations if the local optimizer exists.”
Content-Centric Sparse Multicast Beamforming for Cache-Enabled Cloud RAN (2015, methodology): “The subproblem in each iteration is an SDP problem and can be solved using a generic SDP solver.”