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Found 10 papers, 3 papers with code
Constant-Factor Approximation Algorithms for Socially Fair $k$-Clustering
We study approximation algorithms for the socially fair $(\ell_p, k)$-clustering problem with $m$ groups, whose special cases include the socially fair $k$-median ($p=1$) and socially fair $k$-means ($p=2$) problems.
Maximizing Determinants under Matroid Constraints
Our main result is a new approximation algorithm with an approximation guarantee that depends only on the dimension $d$ of the vectors and not on the size $k$ of the output set.
Effect of discrete breathers on the specific heat of a nonlinear chain
A nonlinear chain with six-order polynomial on-site potential is used to analyze the evolution of the total to kinetic energy ratio during development of modulational instability of extended nonlinear vibrational modes.
Pattern Formation and Solitons Materials Science Mathematical Physics Mathematical Physics 37Mxx, 65P10, 65P40
Multi-Criteria Dimensionality Reduction with Applications to Fairness
Our main result is an exact polynomial-time algorithm for the two-criterion dimensionality reduction problem when the two criteria are increasing concave functions.
The Price of Fair PCA: One Extra Dimension
This motivates our study of dimensionality reduction techniques which maintain similar fidelity for A and B.
Dynamic Resource Allocation in the Cloud with Near-Optimal Efficiency
To ensure these service requirements, cloud offerings often come with a service level agreement (SLA) between the provider and the users.
Data Structures and Algorithms Distributed, Parallel, and Cluster Computing Computer Science and Game Theory
Efficient algorithms for robust submodular maximization under matroid constraints
In this work, we consider robust submodular maximization with matroid constraints.
Approximation Algorithms for D-optimal Design
For the combinatorial experimental design problem, the goal is to pick $k$ out of the given $n$ experiments so as to make the most accurate estimate of the unknown parameters, denoted as $\hat{\beta}$.
Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design
Our main result is to obtain improved approximation algorithms for the $A$-optimal design problem by introducing the proportional volume sampling algorithm.
A geometric alternative to Nesterov's accelerated gradient descent
The new algorithm has a simple geometric interpretation, loosely inspired by the ellipsoid method.
