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Optimization Techniques for Circuit Design Applications
Published in Charles J. Alpert, Dinesh P. Mehta, Sachin S. Sapatnekar, Handbook of Algorithms for Physical Design Automation, 2008
where each ϵi > 0 is a prespecified scalar. In the above formulation, we have δί = (Δai, Δbi) and Δi = {(Δai, Δbi) ║| (Δai, Δbi) ║ ≤ ϵi}. It is known that the above robust LP can be reformulated as a SOCP [2]. Refs. [1,2,5] have shown that the robust counterpart of some other well-known convex optimization problems can also be reformulated in a finite way as a conic optimization problem, often as an SOCP or SDP. Next we consider a robust formulation of a geometric program.
Latency and Energy Efficient Bio-Inspired Conic Optimized and Distributed Q Learning for D2D Communication in 5G
Published in IETE Journal of Research, 2023
Sridhar Varadala, S. Emalda Roslin
In addition, Conic optimization refers to the convex optimization, which examines issues constituting of the decrease in a convex function over the convergence of an affine subspace (i.e. 5G network involving cellular links and D2D pairs) and a convex cone. Here, the cellular links and D2D pairs belong to convex cone. Figure 3 shows the Bio-inspired Conic Particle Swarm Optimization model.
Seismic Bearing Capacity of Strip Footings Placed Adjacent to Rock Slopes
Published in Journal of Earthquake Engineering, 2023
Rui Zhang, Zukai Liu, Jing Pei Yang, Bingjun Shu, Shixuan Cui, Jiaan He, Yao Xiao
To solve this conic optimization problem, the solver MOSEK (2022) based on the primal-dual interior-point method is used in this paper, which has been proved to be robust and computationally efficient and recommended by many scholars (Kumar and Mohapatra 2017; Makrodimopoulos and Martin 2006; Rahaman and Kumar 2020, 2022; Tang, Toh, and Phoon 2014; Ukritchon and Keawsawasvong 2018, 2019).