1,965 research outputs found
Inexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization
In this paper, we propose an inexact block coordinate descent algorithm for
large-scale nonsmooth nonconvex optimization problems. At each iteration, a
particular block variable is selected and updated by inexactly solving the
original optimization problem with respect to that block variable. More
precisely, a local approximation of the original optimization problem is
solved. The proposed algorithm has several attractive features, namely, i) high
flexibility, as the approximation function only needs to be strictly convex and
it does not have to be a global upper bound of the original function; ii) fast
convergence, as the approximation function can be designed to exploit the
problem structure at hand and the stepsize is calculated by the line search;
iii) low complexity, as the approximation subproblems are much easier to solve
and the line search scheme is carried out over a properly constructed
differentiable function; iv) guaranteed convergence of a subsequence to a
stationary point, even when the objective function does not have a Lipschitz
continuous gradient. Interestingly, when the approximation subproblem is solved
by a descent algorithm, convergence of a subsequence to a stationary point is
still guaranteed even if the approximation subproblem is solved inexactly by
terminating the descent algorithm after a finite number of iterations. These
features make the proposed algorithm suitable for large-scale problems where
the dimension exceeds the memory and/or the processing capability of the
existing hardware. These features are also illustrated by several applications
in signal processing and machine learning, for instance, network anomaly
detection and phase retrieval
Converting some global optimization problems to mixed integer linear problems using piecewise linear approximations
Some global optimization problems are converted to mixed-integer linear problems (MILP) using piecewise-linear approximations in this thesis so that they can be solved using commercial MILP solvers, such as CPLEX. Special attention is given to approximating two-term log-sum functions, which appears frequently in generalized geometric programming problems. Numerical results indicate the proposed approach is sound and efficient --Abstract, page iii
Waveform Optimization for Large-Scale Multi-Antenna Multi-Sine Wireless Power Transfer
Wireless power transfer (WPT) is expected to be a technology reshaping the
landscape of low-power applications such as the Internet of Things,
machine-to-machine communications and radio frequency identification networks.
Although there has been some progress towards multi-antenna multi-sine WPT
design, the large-scale design of WPT, reminiscent of massive multiple-input
multiple-output (MIMO) in communications, remains an open problem. Considering
the nonlinear rectifier model, a multiuser waveform optimization algorithm is
derived based on successive convex approximation (SCA). A lower-complexity
algorithm is derived based on asymptotic analysis and sequential approximation
(SA). It is shown that the difference between the average output voltage
achieved by the two algorithms can be negligible provided the number of
antennas is large enough. The performance gain of the nonlinear model based
design over the linear model based design can be large, in the presence of a
large number of tones.Comment: To appear in the 17th IEEE International Workshop on Signal
Processing Advances in Wireless Communications (SPAWC 2016
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