Applications of Second Order Cone Programming

By L. Turner. 2012

1,046 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a1a14e39ee10e9df0b5aa019129377d9a427407d

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Citations in the corpus

A Convex Primal Formulation for Convex Hull Pricing (2016, unknown): “For x gt ≥ 0 and s gt ≥ 0 , constraint (29) is equivalent to which is a second-order cone constraint [34].”
JuMP: A Modeling Language for Mathematical Optimization (2015, background): “Conicquadratic, also known as second-order cone, optimization generalizes quadratic optimization with constraints of the form ||x||2 ≤ t, where both x and t are decision variables [54].”
Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids (2019, unknown): “interior-point method in polynomial time [38].”
A Minorization-Maximization Method for Optimizing Sum Rate in the Downlink of Non-Orthogonal Multiple Access Systems (2015, background): “For the sake of notational convenience, all parameters about which the quadratic terms are linearized in iteratet are defined as Λ t , [w̄tk, r t k, r t k, v t j,θ t k,k,θ t j,k,θ t N,N ,φ t k,m].”
Polyhedral approximation in mixed-integer convex optimization (2016, background): “’s original motivations for developing the DCP framework was to provide access to powerful solvers for the second-order cone [35],”
Robust SAA (2014, background): “Fortunately, the particular exponential cone constraints (26) can be recast as second-order cone constraints, albeit with constraint complexity growing in both n and N (see [27]).”
Convex relaxation of optimal power flow: A tutorial (2013, unknown): “, [32], [33], [34], [35], [36], [37], [38], [39].”
A Regularization Approach to Learning Task Relationships in Multitask Learning (2014, background): “Moreover , we may also reformulate problem (21) as a second-order cone programming (SOCP) problem [Lobo et al. 1998], which is more efﬁcient than SDP when m is large.”
Toward Dual-functional Radar-Communication Systems: Optimal Waveform Design (2017, methodology): “Based on [60], the complexity for using interior-point method to solve the QCQP problems is O (N 3) per iteration.”
Convex Models of Distribution System Reconfiguration (2012, methodology): “As stated, continuous SOCPs can be solved very quickly using interior point algorithms.”
Direct Localization for Massive MIMO (2016, background): “The computational complexity of solving (17) scales as O((QL + l Ml)) [38], where Q and M are the number grid locations and angles respectively, and L is the number of base stations.”
ECOS: An SOCP solver for embedded systems (2013, unknown): “See [9] or [1, Chap.”
Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding (2013, background): “The lasso problem can be formulated as the SOCP [101] minimize (1/2)w + µ1 T t s.t. −t ≤ z ≤ t, 1 − w 2(F z − g)”