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Proximal Algorithms for Distributed Coupled Optimization
In this chapter, we consider a multi-node sharing problem, where each node possesses a local smooth function that is further considered as the...
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An explainable multi-sparsity multi-kernel nonconvex optimization least-squares classifier method via ADMM
Convex optimization techniques are extensively applied to various models, algorithms, and applications of machine learning and data mining. For...
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An effective cost-sensitive sparse online learning framework for imbalanced streaming data classification and its application to online anomaly detection
Class imbalance is one of the most challenging problems in streaming data mining due to its adverse impact on predictive capability of online models....
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Improved Approximation Algorithm for the Asymmetric Prize-Collecting TSP
We present a $$\frac{4\lceil \log (n)\rceil }{0.698\lceil \log (n)\rceil...
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A domain adaptation method by incorporating belief function in twin quarter-sphere SVM
Domain adaptation is a representative problem in transfer learning, which aims to tackle the problem of insufficient labeled data in a target domain...
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Penalized FTRL with Time-Varying Constraints
In this paper we extend the classical Follow-The-Regularized-Leader (FTRL) algorithm to encompass time-varying constraints, through adaptive...
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Primal-Dual Newton’s Method with Steepest Descent for Linear Programming
The primal-dual method for solving linear programming problems is considered. In order to determine the search directions the non-perturbed system of...
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On Minimizing the Energy of a Spherical Graph Representation
Graph representations are the generalization of geometric graph drawings from the plane to higher dimensions. A method introduced by Tutte to...
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Implementation of Simplex Method
All algorithms formulated in this book, such as the simplex algorithm and the dual simplex algorithm, are theoretical or conceptual and cannot be put...
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A Simple Method for Convex Optimization in the Oracle Model
We give a simple and natural method for computing approximately optimal solutions for minimizing a convex function f over a convex set K given by a...
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A Physically Admissible Stokes Vector Reconstruction in Linear Polarimetric Imaging
Polarization encoded images improve on conventional intensity imaging techniques by providing access to additional parameters describing the vector...
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Explicit Assignment and Dynamic Pricing of Macro Online Tasks in Spatial Crowdsourcing
The past decade has witnessed rapid development of wireless communication and ubiquitous availability of mobile devices. Spatial crowdsourcing (SC),...
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Generalized Reduced Simplex Method
Although we always consider the standard LP problem, the LP problems from practice are various. The latter can be transformed into a more general...
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Distributed sparse learning for stochastic configuration networks via alternating direction method of multipliers
As a class of randomized learning algorithms, stochastic configuration networks (SCNs) have demonstrated excellent capabilities in various...
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Online Incentive Mechanism Design for Real-Time Decision Making: Case Study of Collaborative Task Offloading in Mobile Edge Computing
In Chap. 3 , a nonlinear online incentive mechanism for task offloading under Internet of Things (IoT)...
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The Notion of the Quasicentral Path in Linear Programming
The notion of the central path plays an important role in the development of most primal-dual interior-point algorithms. In this work we prove that a...
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A Backward-Characteristics Monotonicity Preserving Method for Stiff Transport Problems
Convection-diffusion problems in highly convective flows can exhibit complicated features such as sharp shocks and shear layers which involve steep...
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ADMM-TGV image restoration for scientific applications with unbiased parameter choice
Image restoration via alternating direction method of multipliers (ADMM) has gained large interest within the last decade. Solving standard problems...
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Addressing Coupled Constrained Reinforcement Learning via Interative Iteration Design
Coupled constraints are a natural setting in many programming problems, such as edge computing, which makes agents more perplexed when updating...
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Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise
We study the problem of deconvolution for light-sheet microscopy, where the data is corrupted by spatially varying blur and a combination of Poisson...
