Reading List 2015.6.16

Semidefinite Relaxation of Quadratic Optimization Problems:
Main contribution of this work transform the QCQP problem into SDP problem by ignoring the rank 1 constraint as an approximation.

Subsampling Algorithms for Semidefinite Programming:
An algorithm to solve spectral norm minimization problem as below:

All matrix parameters are semidefinite.
Based on stochastic decent algorithm, this paper propose an stochastic approximation algorithm where the gradient is approximated using randomized matrix multiplication and randomized low-rank approximation.

Uniform Sampling for Matrix Approximation:
A uniform sampling methods to approximate leverage score.

Simple and Deterministic Matrix Sketching:
A streaming algorithm to maintain a sketch low dimension B so that $A^T A \approx B^T B$ and this algorithm is perfectly parallelizable.

Code Generation for Embedded Second-Order Cone Programming:
This article proposes a parser/generator which only needs to map the parameters to the structure of standard form and then can be solved by custom solver. This paper only gives results for second-order cone programming, lacking semidefinite programming.
QCML is a python toolbox for this kind of matrix stuffing.

Performance Analysis of Cooperative Wireless Networks with Unreliable Backhaul Links:
This paper propose a closed-form for sum of independent bernoulli-weighted exponential random variables which characterizes the outage performance.