A case study of a district's intended and unintended results connected to the implementation of a required ninth-grade algebra 1 strategic initiative

There is a long history of debate around mathematics standards, reform efforts, and accountability. This research identified ways that national expectations and context drive local implementation of mathematics reform efforts and identified the external and internal factors that impact teachers’ acceptance or resistance to policy implementation at the local level. This research also adds to the body of knowledge about acceptance and resistance to policy implementation efforts. This case study involved the analysis of documents to provide a chronological perspective, assess the current state of the District’s mathematics reform, and determine the District’s readiness to implement the Common Core Curriculum. The school system in question has continued to struggle with meeting the needs of all students in Algebra 1. Therefore, the results of this case study will be useful to the District’s leaders as they include the compilation and analysis of a decade’s worth of data specific to Algebra 1.

Tensor Completion for Multidimensional Inverse Problems with Applications to Magnetic Resonance Relaxometry

This thesis deals with tensor completion for the solution of multidimensional inverse problems. We study the problem of reconstructing an approximately low rank tensor from a small number of noisy linear measurements. New recovery guarantees, numerical algorithms, non-uniform sampling strategies, and parameter selection algorithms are developed. We derive a fixed point continuation algorithm for tensor completion and prove its convergence. A restricted isometry property (RIP) based tensor recovery guarantee is proved. Probabilistic recovery guarantees are obtained for sub-Gaussian measurement operators and for measurements obtained by non-uniform sampling from a Parseval tight frame. We show how tensor completion can be used to solve multidimensional inverse problems arising in NMR relaxometry. Algorithms are developed for regularization parameter selection, including accelerated k-fold cross-validation and generalized cross-validation. These methods are validated on experimental and simulated data. We also derive condition number estimates for nonnegative least squares problems. Tensor recovery promises to significantly accelerate N-dimensional NMR relaxometry and related experiments, enabling previously impractical experiments. Our methods could also be applied to other inverse problems arising in machine learning, image processing, signal processing, computer vision, and other fields.