performance via convex optimization
Recovering Structured Signals in High Dimensions via Non-Smooth Convex Optimization: Precise Performance Analysis
... non-smooth convex optimization methods have emerged as a powerful tool to extract those structures, since they are often computationally e ffi cient, and also they o ff er enough flexibility while ...
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Sparse Recovery via Convex Optimization
... On the practical end, we truly recommend using the two-step refinement dis- cussed in Section 2.5—the reprojection step—as this really tends to enhance the performance. We anticipate that other tweaks of this kind ...
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Online Convex Optimization Using Predictions
... the performance of the algorithm with realistic prediction errors (which tend to be correlated, since an overestimate in one period is likely followed by another overestimate) and further does not help inform the ...
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Robust design of Farrow-structure-based steerable broadband beamformers with sparse tap weights via convex optimization
... weights via convex optimization by considering some a priori knowledge of microphone ...worst-case performance (WCP) optimization has been successfully applied to the design of robust ...
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A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization
... ∞ performance for multiple weighted sen- sitivity functions are presented using a discrete-time RST controller ...∞ performance and closed-loop stability for linear systems that are subject to nonlinear ...
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Distributed and parallel methods for structural convex optimization
... separable convex optimization (see Chapters 4 and ...separable convex problems when each inner subproblem is solved ...separable convex optimization based on Nesterov’s smoothing ...
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Adaptive Bound Optimization for Online Convex Optimization
... This performance difference is not merely a weakness in the regret bounds for ordinary gradient descent, but is a difference in actual regret. In concurrent work (Streeter & McMahan, 2010), we showed that for ...
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On Lower and Upper Bounds in Smooth and Strongly Convex Optimization
... any convex quadratic function can be found using no more than O(d) first-order ...large-scale optimization problems, such as gradient descent and its ...attainable performance of such algorithms, we ...
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Metric learning with convex optimization
... the performance of many algorithms such as the simplest k-NN classifier and some clustering ...as optimization of the inner product of the linear transformation matrix and its transpose, the formulation is ...
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Optimal Tensor Methods in Smooth Convex and Uniformly Convex Optimization
... Figure 2 shows the simulation results for the logistic regression problem in (23) for various datasets. Similarly as in Figure 1, we compare the performance of Algorithm 1, and the accelerated tensor method in ...
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Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization
... In this paper, we summarize and categorize the constraints needed to solve various speed planning problems in different scenarios as the requirements for speed planners design and metrics to measure the capacity of the ...
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Robust H-infinity Controller Design Using Frequency-Domain Data via Convex Optimization
... ∞ performance specification using frequency-domain ...∞ performance is presented in ...non-convex optimization problem to find a local ...non-convex optimization method. A ...
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Clustering Partially Observed Graphs via Convex Optimization
... uses convex optimization; its basis is a reduction of disagreement minimization to the problem of recovering an (unknown) low-rank matrix and an (unknown) sparse matrix from their partially observed ...the ...
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Scalable analysis of nonlinear systems using convex optimization
... The structure of the dynamics that are chosen for TCP and AQM are usually based on a gradient algorithm to guarantee convergence [34]; when the delays that are ubiquitously present in the system are ignored, a Lyapunov ...
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An Online Convex Optimization Approach to Blackwell's Approachability
... the performance of online learning algorithms, and its scope has been greatly extended ...Online Convex Optimization (OCO) framework, where Nature’s discrete action is replaced by the choice of a ...
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A Non–Convex Optimization Approach to Correlation Clustering
... non-convex optimization approach to correla- tion clustering using the Frank-Wolfe (FW) ...the performance on both synthetic and real world data ...
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IIR Digital Filter Design Using Convex Optimization
... It can be observed from (5.25) that if is fixed, we can force the poles to move towards the origin (i.e., → 0) by suppressing . When all poles lie on the origin (i.e., = 0), we have = 0 and the designed IIR digital ...
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SnapVX: A Network-Based Convex Optimization Solver
... SnapVX is a high-performance solver for convex optimization problems defined on networks. For problems of this form, SnapVX provides a fast and scalable solution with guaranteed global convergence. ...
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Misaligned Image Integration Based on Convex Optimization
... A new method can be introduced to solve this problem. The proposed system combining two images and produce an image with clear and natural color. The two images are flash image and long-exposure image [1]. The ...
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Formulating Cyber-Security as Convex Optimization Problems
... propose optimization- based algorithms that can be used to predict in real-time how an at- tacker may try to compromise a cyber-mission with a limited amount of resources, based on a model that takes into account ...
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