Georgia Institute of Technology College of Sciences

Given a contact structure on a bordered 3-manifold, we describe an invariant which takes values in the bordered sutured Floer homology of the manifold. This invariant satisfies a nice gluing formula, and recovers the Oszvath-Szabo contact class in Heegaard Floer homology. This is joint work with Alishahi, Foldvari, Hendricks, Licata, and Vertesi.

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Meeting ID: 980 3103 5804

Passcode: 196398

Semidefinite programming is a powerful optimization tool, which involves optimizing linear functions on a slice of the positive semidefinite matrices. Locally PSD matrices are a natural relaxation of the PSD matrices which can be useful in reducing the space required for semidefinite optimization. We use the theory of hyperbolic polynomials to give precise quantitative bounds on the quality of the approximation resulting from optimizing over the locally-psd cone instead of the PSD cone.

Kernel principal component analysis (KPCA) is a popular non-linear dimensionality reduction technique, which generalizes classical linear PCA by finding functions in a reproducing kernel Hilbert space (RKHS) such that the function evaluation at a random variable $X$ has a maximum variance. Despite its popularity, kernel PCA suffers from poor scalability in big data scenarios as it involves solving a $n \times  n$ eigensystem leading to the computational complexity of $O(n^3)$ with $n$ being the number of samples. To address this issue, in this work, we consider a random feature approximation to kernel PCA which requires solving an $m \times m$ eigenvalue problem and therefore has a computational complexity of $O(m^3+nm^2)$, implying that the approximate method is computationally efficient if $m$ < $n$ with $m$ being the number of random features. The goal of this work is to investigate the trade-off between computational and statistical behaviors of approximate KPCA, i.e., whether the computational gain is achieved at the cost of statistical efficiency. We show that the approximate KPCA is both computationally and statistically efficient compared to KPCA in terms of the error associated with reconstructing a kernel function based on its projection onto the corresponding eigenspaces.

Link to Cisco Webex meeting: https://gatech.webex.com/gatech/j.php?MTID=mdd4512d3d11623149a0bd46d9fc086c8

A classical question in extremal graph theory asks to maximize the number of induced copies of a given graph or tournament in a large host graph, often expressed as a density. A simple averaging argument shows that the limit of this density exists as the host graph is allowed to grow. Razborov's flag algebra method is well suited to generate bounds on these quantities with the help of semidefinite programming. We will explore this method for a few small examples, and see how to modify it to fit our questions. The extremal graphs show some beautiful structures, sometimes fractal like, sometimes quasi random and sometimes even a combination of both.