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We show how to sketch semidefinite programs (SDPs) using positive maps in order to reduce their dimension. More precisely, we use Johnsonhyp{}Lindenstrauss transforms to produce a smaller SDP whose solution preserves feasibility or approximates the value of the original problem with high probability. These techniques allow to improve both complexity and storage space requirements. They apply to problems in which the Schatten 1-norm of the matrices specifying the SDP and also of a solution to the problem is constant in the problem size. Furthermore, we provide some results which clarify the limitations of positive, linear sketches in this setting.
This paper introduces a new interior point method algorithm that solves semidefinite programming (SDP) with variable size $n times n$ and $m$ constraints in the (current) matrix multiplication time $m^{omega}$ when $m geq Omega(n^2)$. Our algorithm i
Sampling-based motion planning algorithms such as RRT* are well-known for their ability to quickly find an initial solution and then converge to the optimal solution asymptotically. However, the convergence rate can be slow for highdimensional planni
Dimensionality reduction is a classical technique widely used for data analysis. One foundational instantiation is Principal Component Analysis (PCA), which minimizes the average reconstruction error. In this paper, we introduce the multi-criteria di
We present a randomized primal-dual algorithm that solves the problem $min_{x} max_{y} y^top A x$ to additive error $epsilon$ in time $mathrm{nnz}(A) + sqrt{mathrm{nnz}(A)n}/epsilon$, for matrix $A$ with larger dimension $n$ and $mathrm{nnz}(A)$ nonz
In this article we introduce the use of recently developed min/max-plus techniques in order to solve the optimal attitude estimation problem in filtering for nonlinear systems on the special orthogonal (SO(3)) group. This work helps obtain computatio