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Traffic managements for household travels in congested morning commute
Due to the high car ownership cost or car ownership restrictions in many major cities, household travels, which include multiple trips for all the household members, become very common. One typicalExpand
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Comparison of several fast algorithms for projection onto an ellipsoid
TLDR
We rewrite the convex projection problem as a constrained convex optimization problem with separable objective functions, which enables the use of the alternating direction method of multipliers (ADMM). Expand
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An efficient projection method for nonlinear inverse problems with sparsity constraints
In this paper, we propose a modification of the accelerated projective steepest descent method for solving nonlinear inverse problems with an $\ell_1$ constraint on the variable, which was recentlyExpand
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A proximal alternating linearization method for minimizing the sum of two convex functions
In this paper, we develop a novel alternating linearization method for solving convex minimization whose objective function is the sum of two separable functions. The motivation of the paper is toExpand
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Convergence Analysis of Alternating Direction Method of Multipliers for a Class of Separable Convex Programming
The purpose of this paper is extending the convergence analysis of Han and Yuan (2012) for alternating direction method of multipliers (ADMM) from the strongly convex to a more general case. UnderExpand
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Local Linear Convergence of the Alternating Direction Method of Multipliers for Nonconvex Separable Optimization Problems
TLDR
In this paper, we consider the convergence rate of the alternating direction method of multipliers for solving the nonconvex separable optimization problems and compare it with some state-of-the-art algorithms. Expand
The convergence rate analysis of the symmetric ADMM for the nonconvex separable optimization problems
The symmetric alternating direction method of multipliers is an efficient algorithm, which updates the Lagrange multiplier twice at each iteration and the variables are treated in a symmetric manner.Expand
An inexact proximal gradient algorithm with extrapolation for a class of nonconvex nonsmooth optimization problems
In this paper, we propose an inexact version of proximal gradient algorithm with extrapolation for solving a class of nonconvex nonsmooth optimization problems. Specifically, the subproblem inExpand