Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 59 for “"Iterative algorithms"”.
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Iterative algorithms for lossy source coding
This thesis explores the problems of lossy source coding and information embedding. For lossy source coding, we analyze low density parity check (LDPC) codes and low density generator matrix (LDGM) codes for quantization under a Hamming distortion. We prove that LDPC codes can achieve the …
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Contraction maps and applications to the analysis of iterative algorithms
… for bounding the running time of a big class of iterative algorithms used to solve non-convex problems. But when we use the natural distance metric, of the spaces that we are working on, the applicability of Banach's Fixed Point Theorem becomes limited. The reason is that only few functions have …
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Iterative algorithms for optimal signal reconstruction and parameter identification given noisy and incomplete data
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982.
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Iterative algorithms for a joint pricing and inventory control problem with nonlinear demand functions
… problems and develop computationally efficient algorithms that aim to tackle and optimally solve these problems in a finite amount of time. In the first half of the thesis we consider the joint pricing and inventory control problem in a deterministic and multiperiod setting utilizing the popular …
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The Performance of Preconditioned Iterative Methods in Computational Electromagnetics
… of the resulting matrix equation. By using iterative algorithms, the analysis of scatterers that are an order of magnitude larger electrically may be feasible.
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Blind Multichannel Image Deconvolution and Optimum Sparse Approximations
… is the development of a new class of iterative algorithms for identifying sparse elements of the convex and compact set. We show that the algorithm has good convergence properties through a detailed theoretical analysis and demonstrate its performance on some examples.
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Numerical solutions of matrix equations arising in model reduction of large-scale linear -time -invariant systems
This thesis presents and analyzes new algorithms for matrix equations arising from model reduction of linear-time-invariant (LTI) systems. Such systems arise in a variety of areas, especially in circuit simulation. When an integrated circuit has millions of devices, performing a full-system …
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A hardware acceleration technique for gradient descent and conjugate gradient
Gradient descent, conjugate gradient, and other iterative algorithms are a powerful class of algorithms; however, they can take a long time for conver- gence. Baseline accelerator designs feature insu cient coverage of operations and do not work well on the problems we target. In this thesis we …
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Robust computational methods to simulate slow-fast dynamical systems governed by predator-prey models
… parts of such schemes are treated by using iterative algorithms, which are known for their superlinear convergence, such as the Jacobian-Free Newton-Krylov (JFNK) and the Anderson’s Acceleration (AA) fixed point methods.
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Microscopy of Gold Microcrystals by Coherent X -Ray Diffractive Imaging
… the diffraction from such an experiment and iterative phasing the coherent X-ray diffraction (CXD) pattern, we can create a sort of lenless X-ray microscope, where the lens is replaced by a calculation. The iterative algorithms explored include Error Reduction and Fienup's Hybrid Input/Output …
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Reconstruction from non-uniform samples
… sinc functions to obtain faster convergence in iterative algorithms for reconstruction of band-limited signals from non-uniform samples.
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Building compressed sensing systems : sensors and analog-to-information converters
… of CS-based systems is that they employ iterative algorithms to recover the signal. Since these algorithms are slow, the hardware solution has become crucial for higher performance and speed. In this work, we also implement a suitable CS reconstruction algorithm in hardware.
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Graph similarity and matching
… then focuses on an interesting class of iterative algorithms that use the structural similarity of local neighborhoods to derive pairwise similarity scores between graph elements. We have developed a new similarity measure that uses a linear update to generate both node and edge …
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System Optimization and Iterative Image Reconstruction in Photoacoustic Computed Tomography for Breast Imaging
… (3D) object such as the breast, iterative reconstruction algorithms are often utilized to alleviate the need to collect densely sampled measurement data hence a long scanning time. However, the heavy computation burden associated with iterative algorithms largely hinders its …
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Dynamic node clustering in hierarchical optical data center network architectures
… and network scalability. We present four algorithms, two deterministic greedy and two stochastic iterative, and discuss the tradeoffs of their use. Our results draw two main conclusion: 1) Stochastic iterative algorithms are more suitable for dynamic traffic based reconfiguration 2) Fast …
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Physics-assisted machine learning for X-ray imaging
… adopted for 2D and 3D reconstruction. Unlike iterative algorithms which require a distribution that is known a priori, deep reconstruction networks can learn a prior distribution through sampling the statistical properties of the training distributions. In this thesis, we develop a …
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Robust implementation of algorithms for distributed generation control of small-footprint power systems
… maker. In particular, we discuss a class of iterative algorithms which are capable of coordinating a set of DGRs in order to collectively achieve a predetermined goal. We begin by formulating an unconstrained algorithm which we later extend to account for individual DGR capacity constraints. …
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Amplitude sampling for signal representation
… samples is shown to be possible through iterative algorithms. If both time and amplitude are restricted to equally-spaced values, then the sampling strategy, referred to as lattice sampling, simultaneously uses both uniform amplitude and uniform time sampling. A class of bandlimited …
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Adaptation of the MapReduce programming framework to compute-intensive data-analytics kernels
… well as scientific computing applications. These algorithms stem from domains as diverse as web analysis and social networks, machine learning and data mining, text analysis, bio-informatics, astronomy image analysis, business analytics, large scale graph algorithms, image/video processing and …
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Dynamic resource allocation in CDMA cellular communications systems
… mobile. We develop sequential and distributed iterative algorithms for solving a more general version of this integer programming problem and show that they find the optimal solution in a finite number of iterations which is polynomial in the number of power levels and the number of mobiles.
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