Now showing items 1-10 of 32
Rank-Two Relaxation Heuristics for Max-Cut and Other Binary Quadratic Programs
Semidefinite relaxation for certain discrete optimization problems involves replacing a vector-valued variable by a matrix-valued one, producing a convex program while increasing the number of variables by an order of ...
Simultaneous Structure Factor and Contrast Transfer Function Parameter Determination in Transmission Electron Microscopy
We present a new method that allows a fully automated simultaneous determination of the structure factor and the parameters of the Contrast Transfer Function (CTF) and noise function. No previous knowledge of the structure ...
Dose-Volume-Based IMRT Fluence Optimization: A Fast Least-Squares Approach With Differentiability
In intensity-modulated radiation therapy (IMRT) for cancer treatment, the most commonly used metric for treatment prescriptions and evaluations is the so-called dose volume constraint (DVC). These DVCs induce much needed ...
When is Missing Data Recoverable?
Suppose a non-random portion of a data vector is missing. With some minimal prior knowledge about the data vector, can we recover the missing portion from the available one? In this paper, we consider a linear programming ...
A Fixed-Point Continuation Method for L_1-Regularization with Application to Compressed Sensing
We consider solving minimization problems with L_1-regularization: min ||x||_1 + mu f(x) particularly for f(x) = (1/2)||Ax-b||M2, where A is m by n and m < n. Our goal is to construct efficient and robust algorithms for ...
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
We propose, analyze and test an alternating minimization algorithm for recovering images from blurry and noisy observa- tions with total variation (TV) regularization. This algorithm arises from a new half-quadratic model ...
Comparison of Two Sets of First-order Conditions as Bases of Interior-Point Newton Methods for Optimization with Simple Bounds
In this paper, we compare the behavior of two Newton interior-point methods derived from two different first-order necessary conditions for the same nonlinear optimization problem with simple bounds. One set of conditions ...
User's Guide for LMaFit: Low-rank Matrix Fitting
This User's Guide describes the functionality and basic usage of the Matlab package LMaFit for low-rank matrix optimization. It also briefly explains the formulations and algorithms used.
A Simple Proof for Recoverability of L1-Minimization (II): the Nonnegativity Case
When using L1 minimization to recover a sparse, nonnegative solution to a under-determined linear system of equations, what is the highest sparsity level at which recovery can still be guaranteed? Recently, Donoho and ...
On Theory of Compressive Sensing via L_1-Minimization: Simple Derivations and Extensions
Compressive (or compressed) sensing (CS) is an emerging methodology in computational signal processing that has recently attracted intensive research activities. At present, the basic CS theory includes recoverability and ...