Approximation Algorithms for D-optimal Design

Design Algorithms for Doppler MTI and Filters (Using Chebyshev Approximation)

Design Algorithms for Doppler MTI and Filters (Using Chebyshev Approximation)

Approximate Positive Correlated Distributions and Approximation Algorithms for D-optimal Design

Experimental design is a classical area in statistics [21] and has also found new applications in machine learning[2]. In the combinatorial experimental design problem, the aim is to estimate an unknown m-dimensional vector x from linear measurements where a Gaussian noise is introduced in eachmeasurement. The goal is to pick k out of the given n experiments so as to make the most accurate esti...

design algorithms for doppler mti and filters (using chebyshev approximation)

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Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design

We study the A-optimal design problem where we are given vectors v1, . . . , vn ∈ R, an integer k ≥ d, and the goal is to select a set S of k vectors that minimizes the trace of (∑ i∈S viv > i )−1 . Traditionally, the problem is an instance of optimal design of experiments in statistics (Pukelsheim (2006)) where each vector corresponds to a linear measurement of an unknown vector and the goal i...