Convex optimization ≈ Convex optimization
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Fractional Programming for Communication Systems—Part I: Power Control and Beamforming Open
This two-part paper explores the use of FP in the design and optimization of communication systems. Part I of this paper focuses on FP theory and on solving continuous problems. The main theoretical contribution is a novel quadratic transf…
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Data-Driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations Open
We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) prob…
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Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning Open
Unmanned aerial vehicles (UAVs) have been recently considered as means to provide enhanced coverage or relaying services to mobile users (MUs) in wireless systems with limited or no infrastructure. In this paper, a UAV-based mobile cloud c…
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Robust and Secure Wireless Communications via Intelligent Reflecting Surfaces Open
In this paper, intelligent reflecting surfaces (IRSs) are employed to enhance the physical layer security in a challenging radio environment. In particular, a multi-antenna access point (AP) has to serve multiple single-antenna legitimate …
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CVXPY: A Python-Embedded Modeling Language for Convex Optimization. Open
CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by …
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Fractional Programming for Communication Systems—Part II: Uplink Scheduling via Matching Open
This two-part paper develops novel methodologies for using fractional\nprogramming (FP) techniques to design and optimize communication systems. Part\nI of this paper proposes a new quadratic transform for FP and treats its\napplication fo…
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Asynchronous Federated Optimization Open
Federated learning enables training on a massive number of edge devices. To improve flexibility and scalability, we propose a new asynchronous federated optimization algorithm. We prove that the proposed approach has near-linear convergenc…
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Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint l2,1 Norm Open
To improve the detection ability of infrared small targets in complex backgrounds, a novel method based on non-convex rank approximation minimization joint l2,1 norm (NRAM) was proposed. Due to the defects of the nuclear norm and l1 norm, …
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Sparse Regularization via Convex Analysis Open
Sparse approximate solutions to linear equations are classically obtained via L1 norm regularized least squares, but this method often underestimates the true solution. As an alternative to the L1 norm, this paper proposes a class of non-c…
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Achievable Rate Maximization by Passive Intelligent Mirrors Open
This paper investigates the use of a Passive Intelligent Mirrors (PIM) to operate a multi-user MISO downlink communication. The transmit powers and the mirror reflection coefficients are designed for sum-rate maximization subject to indivi…
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Non-convex Optimization for Machine Learning Open
A vast majority of machine learning algorithms train their models and perform\ninference by solving optimization problems. In order to capture the learning\nand prediction problems accurately, structural constraints such as sparsity or\nlo…
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Robust Beamforming Design for Intelligent Reflecting Surface Aided MISO Communication Systems Open
Perfect channel state information (CSI) is challenging to obtain due to the limited signal processing capability at the intelligent reflection surface (IRS). This is the first work to study the worst-case robust beamforming design for an I…
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Resource Allocation for IRS-Assisted Full-Duplex Cognitive Radio Systems Open
In this article, we investigate the resource allocation design for intelligent reflecting surface (IRS)-assisted full-duplex (FD) cognitive radio systems. In particular, a secondary network employs an FD base station (BS) for serving multi…
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Wireless-Powered Communications With Non-Orthogonal Multiple Access Open
We study a wireless-powered uplink communication system with non-orthogonal\nmultiple access (NOMA), consisting of one base station and multiple energy\nharvesting users. More specifically, we focus on the individual data rate\noptimizatio…
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Multi-robot formation control and object transport in dynamic environments via constrained optimization Open
We present a constrained optimization method for multi-robot formation control in dynamic environments, where the robots adjust the parameters of the formation, such as size and three-dimensional orientation, to avoid collisions with stati…
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Relatively Smooth Convex Optimization by First-Order Methods, and Applications Open
The usual approach to developing and analyzing first-order methods for smooth convex optimization assumes that the gradient of the objective function is uniformly smooth with some Lipschitz constant $L$. However, in many settings the diffe…
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A Review of Sparse Recovery Algorithms Open
Nowadays, a large amount of information has to be transmitted or processed. This implies high-power processing, large memory density, and increased energy consumption. In several applications, such as imaging, radar, speech recognition, an…
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Energy Efficient UAV Communication With Energy Harvesting Open
This paper investigates an unmanned aerial vehicle (UAV)-enabled wireless communication system with energy harvesting, where the UAV transfers energy to the users in half duplex or full duplex, and the users harvest energy for data transmi…
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Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator Open
Direct policy gradient methods for reinforcement learning and continuous control problems are a popular approach for a variety of reasons: 1) they are easy to implement without explicit knowledge of the underlying model 2) they are an "end…
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Consensus-ADMM for General Quadratically Constrained Quadratic Programming Open
Non-convex quadratically constrained quadratic programming (QCQP) problems\nhave numerous applications in signal processing, machine learning, and wireless\ncommunications, albeit the general QCQP is NP-hard, and several interesting\nspeci…
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q -Hermite Hadamard inequalities and quantum estimates for midpoint type inequalities via convex and quasi-convex functions Open
In this paper, we prove the correct q-Hermite–Hadamard inequality, some new q-Hermite–Hadamard inequalities, and generalized q-Hermite–Hadamard inequality. By using the left hand part of the correct q-Hermite–Hadamard inequality, we have a…
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An Online Convex Optimization Approach to Proactive Network Resource Allocation Open
Existing approaches to online convex optimization (OCO) make sequential\none-slot-ahead decisions, which lead to (possibly adversarial) losses that\ndrive subsequent decision iterates. Their performance is evaluated by the\nso-called regre…
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No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis Open
In this paper we develop a new framework that captures the common landscape underlying the common non-convex low-rank matrix problems including matrix sensing, matrix completion and robust PCA. In particular, we show for all above problems…
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Joint Optimization of a UAV's Trajectory and Transmit Power for Covert Communications Open
This work considers covert communications in the context of unmanned aerial\nvehicle (UAV) networks, aiming to hide a UAV for transmitting critical\ninformation out of a scenario that is monitored and where communication is not\nallowed. S…
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Energy Efficiency Optimization for NOMA With SWIPT Open
IEEE The combination of simultaneous wireless information and power transfer (SWIPT) and non-orthogonal multiple access (NOMA) is a potential solution to improve spectral efficiency (SE) and energy efficiency (EE) of the upcoming fifth gen…
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Gradient Sparsification for Communication-Efficient Distributed Optimization Open
Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the communication overhead for exchanging information such as sto…
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Convergence Rate of Distributed ADMM over Networks Open
We propose a new distributed algorithm based on alternating direction method of multipliers (ADMM) to minimize sum of locally known convex functions using communication over a network. This optimization problem emerges in many applications…
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Dynamic Control of Agents Playing Aggregative Games With Coupling Constraints Open
We address the problem to control a population of noncooperative heterogeneous agents, each with convex cost function depending on the average population state, and all sharing a convex constraint, toward an aggregative equilibrium. We ass…
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On the Uplink Max–Min SINR of Cell-Free Massive MIMO Systems Open
International audience
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Optimal algorithms for smooth and strongly convex distributed optimization in networks Open
In this paper, we determine the optimal convergence rates for strongly convex and smooth distributed optimization in two settings: centralized and decentralized communications over a network. For centralized (i.e. master/slave) algorithms,…