Distributed Primal-Dual Method for Convex Optimization With Coupled Constraints

Su, Yanxu; Wang, Qingling; Sun, Changyin

doi:10.1109/tsp.2021.3123888

Cited by 16 publications

(6 citation statements)

References 43 publications

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“…We consider constraint-coupled problems consisting of N subproblems coupled via both equality and inequality constraints [40,41]. We will assume the coupling constraints are themselves separable, i.e.…”

Section: A Problem Statementmentioning

confidence: 99%

Dynamic Locational Marginal Emissions via Implicit Differentiation

Valenzuela

Degleris

Gamal

et al. 2024

IEEE Trans. Power Syst.

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Locational marginal emissions rates (LMEs) estimate the rate of change in emissions due to a small change in demand in a transmission network, and are an important metric for assessing the impact of various energy policies or interventions. In this work, we develop a new method for computing the LMEs of an electricity system via implicit differentiation. The method is model agnostic; it can compute LMEs for any convex optimization-based dispatch model, including some of the complex dispatch models employed by system operators in real electricity systems. In particular, this method lets us derive LMEs for dynamic dispatch models, i.e., models with temporal constraints such as ramping and storage. Using real data from the U.S. electricity system, we validate the proposed method against a state-of-the-art merit-order-based method and show that incorporating dynamic constraints improves model accuracy by 8.2%. Finally, we use simulations on a realistic 240-bus model of WECC to demonstrate the flexibility of the tool and the importance of incorporating dynamic constraints. Namely, static LMEs and dynamic LMEs exhibit a normalized average RMS deviation of 28.40%, implying dynamic constraints are essential to accurately modeling emissions rates.

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Section: A Problem Statementmentioning

confidence: 99%

Dynamic Locational Marginal Emissions via Implicit Differentiation

Valenzuela

Degleris

Gamal

et al. 2024

IEEE Trans. Power Syst.

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“…To meet the security needs of airports and high‐level matches, one must solve the problem of massive UAV jamming and improve the jamming rate and success rate. Some works have applied commonly used convex optimization methods to the distributed jamming problems of satellite navigation, such as the distributed sub gradient method [15] and primal‐dual method [16]. These methods have a sufficient theoretical basis and application support and can improve the jamming speed to a certain extent.…”

Section: Related Workmentioning

confidence: 99%

“…The existing optimization methods mainly use convex optimization theory [15,16], deep learning theory [17,18], multidimensional derivation technology [19,20] and other methods to optimize the system parameters for one or more indicators. In this kind of optimization system, the index has obvious two-way, while the massive UAV jamming optimization has the characteristics of target free feedback.…”

Section: Introductionmentioning

confidence: 99%

Distributed massive UAV jamming optimization algorithm with artificial bee colony

Chen

2022

IET Communications

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With the massive application of unmanned aerial vehicles (UAVs) in disaster relief, fire protection, security and other fields, jamming countermeasures to UAVs in airports and competition venues have become the core link of security in high‐level matches and important places. The existing jamming sources are mainly single point jamming. Due to the shielding of buildings, it is impossible to fully cover the venue, and the existing suppressed jamming will affect the communication broadcasting. Here, a distributed optimal jamming method for massive UAVs is proposed. Through the identification and positioning of massive UAVs, using the location information of distributed jamming sources, the UAV jamming architecture based on satellite navigation is constructed. Combined with tabu search artificial bee colony theory, the distributed optimal jamming of UAVs is realized, which solves the problems of few jamming targets, slow speed and low success rate of the existing optimal jamming methods. The proposed method is compared with the existing methods in three aspects: communication frequency, jamming trajectory deviation and optimization speed. The simulation results show that the proposed method effectively improves the jamming success rate and jamming speed and can meet the application requirements of massive UAV jamming in the region.

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“…One can observes that the dual of (P1) has the same form with the classical decentralized unconstrained optimizaton (DUO) problem, leading to the natural idea of applying existing DUO algorithms to its dual. This approach has been adopted in numerous previous works [8][9][10][11][12][13][14][15][16][17][18][19][20]. However, a key challenge lies in dealing with the dual gradient or dual Email: jingwangli@outlook.com, houshengsu@gmail.com.…”

Section: Introductionmentioning

confidence: 99%

Distributed Primal-Dual Mirror Dynamics for Constraint-Coupled Optimization

2023

Lecture Notes in Electrical Engineering

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We propose an inexact decentralized dual gradient tracking method (iDDGT) for decentralized optimization problems with a globally coupled equality constraint. Unlike existing algorithms that rely on either the exact dual gradient or an inexact one obtained through single-step gradient descent, iDDGT introduces a new approach: utilizing an inexact dual gradient with controllable levels of inexactness. Numerical experiments demonstrate that iDDGT achieves significantly higher computational efficiency compared to state-of-the-art methods. Furthermore, it is proved that iDDGT can achieve linear convergence over directed graphs without imposing any conditions on the constraint matrix. This expands its applicability beyond existing algorithms that require the constraint matrix to have full row rank and undirected graphs for achieving linear convergence.

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Distributed Primal-Dual Method for Convex Optimization With Coupled Constraints

Cited by 16 publications

References 43 publications

Dynamic Locational Marginal Emissions via Implicit Differentiation

Dynamic Locational Marginal Emissions via Implicit Differentiation

Distributed massive UAV jamming optimization algorithm with artificial bee colony

Distributed Primal-Dual Mirror Dynamics for Constraint-Coupled Optimization

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