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Let $K$ be a finite simplicial, cubical, delta or CW complex. The persistence map $\mathrm{PH}$ takes a filter $f:K \rightarrow \mathbb{R}$ as input and returns the barcodes $\mathrm{PH}(f)$ of the associated sublevel set persistent…

Computational Geometry · Computer Science 2021-10-29 Jacob Leygonie , Gregory Henselman-Petrusek

This article presents an error analysis of the symmetric linear/bilinear partially penalized immersed finite element (PPIFE) methods for interface problems of Helmholtz equations. Under the assumption that the exact solution possesses a…

Numerical Analysis · Mathematics 2020-06-22 Ruchi Guo , Tao Lin , Yanping Lin , Qiao Zhuang

Cylindrical algebraic decomposition (CAD) is an important tool for working with polynomial systems, particularly quantifier elimination. However, it has complexity doubly exponential in the number of variables. The base algorithm can be…

Symbolic Computation · Computer Science 2016-10-03 Matthew England , James H. Davenport

We study here several variants of the covariates fine balance problem where we generalize some of these problems and introduce a number of others. We present here a comprehensive complexity study of the covariates problems providing…

Data Structures and Algorithms · Computer Science 2020-09-18 Dorit S. Hochbaum , Asaf Levin , Xu Rao

We obtain the spline recovery method on a $d$-dimensional simplex $T$ that uses as information values and gradients of a function $f$ at the vertices of $T$ and is optimal for recovery of $f({\bf w})$ at every point ${\bf w}$ of an…

Numerical Analysis · Mathematics 2022-12-22 Sergiy Borodachov

The control of large buildings encounters challenges in computational efficiency due to their size and nonlinear components. To address these issues, this paper proposes a Piecewise Affine (PWA)-based distributed scheme for Model Predictive…

Optimization and Control · Mathematics 2026-02-06 Hongyi Li , Jun Xu , Jinfeng Liu

It has recently been shown that the problem of testing global convexity of polynomials of degree four is {strongly} NP-hard, answering an open question of N.Z. Shor. This result is minimal in the degree of the polynomial when global…

Optimization and Control · Mathematics 2019-03-14 Amir Ali Ahmadi , Georgina Hall

We present a complexity reduction algorithm for a family of parameter-dependent linear systems when the system parameters belong to a compact semi-algebraic set. This algorithm potentially describes the underlying dynamical system with…

Systems and Control · Computer Science 2012-09-25 Farhad Farokhi , Henrik Sandberg , Karl H. Johansson

Many probabilistic inference tasks involve summations over exponentially large sets. Recently, it has been shown that these problems can be reduced to solving a polynomial number of MAP inference queries for a model augmented with randomly…

Artificial Intelligence · Computer Science 2013-09-27 Stefano Ermon , Carla P. Gomes , Ashish Sabharwal , Bart Selman

Error bounds have been studied for more than seventy years, beginning with the seminal result of Hoffman (1952) [{\it J. Res. Natl. Bur. Standards}, 49 (1952), 263--265], which establishes an upper bound for the distance from an arbitrary…

Optimization and Control · Mathematics 2026-05-25 Zhou Wei , Michel Thera , Jen-Chih Yao

One of the major problems in modeling natural signals is that signals with very similar structure may locally have completely different measurements, e.g., images taken under different illumination conditions, or the speech signal captured…

Computer Vision and Pattern Recognition · Computer Science 2012-07-19 Nebojsa Jojic , Yaron Caspi , Manuel Reyes-Gomez

We propose an approach to trajectory optimization for piecewise polynomial systems based on the recently proposed graphs of convex sets framework. We instantiate the framework with a convex relaxation of optimal control based on occupation…

Optimization and Control · Mathematics 2025-07-28 Etienne Buehrle , Ömer Şahin Taş , Christoph Stiller

Ising machines can solve combinatorial optimization problems by representing them as energy minimization problems. A common implementation is the probabilistic Ising machine (PIM), which uses probabilistic (p-) bits to represent coupled…

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…

Discrete Mathematics · Computer Science 2020-06-17 Uthaipon Tantipongpipat , Samira Samadi , Mohit Singh , Jamie Morgenstern , Santosh Vempala

The main purpose of this paper is to study the NP-complete subset-sum problem, not in the usual context of time-complexity-based classification of the algorithms (exponential/polynomial), but through a new kind of algorithmic classification…

Computational Complexity · Computer Science 2018-11-20 Antonios Syreloglou

Motivated by the task of clustering either $d$ variables or $d$ points into $K$ groups, we investigate efficient algorithms to solve the Peng-Wei (P-W) $K$-means semi-definite programming (SDP) relaxation. The P-W SDP has been shown in the…

Machine Learning · Statistics 2018-10-23 Carson Eisenach , Han Liu

We present a polynomial-time algorithm that discovers all maximal patterns in a point set, $D\subset\mathbb{R}^k$, that are related by transformations in a user-specified class, $F$, of bijections over $\mathbb{R}^k$. We also present a…

Machine Learning · Computer Science 2022-02-01 David Meredith

Motivated by conforming finite element methods for elliptic problems of second order, we analyze the approximation of the gradient of a target function by continuous piecewise polynomial functions over a simplicial mesh. The main result is…

Numerical Analysis · Mathematics 2018-03-07 Andreas Veeser

We analyze affine reachability problems in dimensions 1 and 2. We show that the reachability problem for 1-register machines over the integers with affine updates is PSPACE-hard, hence PSPACE-complete, strengthening a result by Finkel et…

Computational Complexity · Computer Science 2020-07-03 Stefan Jaax , Stefan Kiefer

In a previous paper it was shown that a machine learning regression problem can be solved within the framework of random function theory, with the optimal kernel analytically derived from symmetry and indifference principles and coinciding…

Machine Learning · Computer Science 2025-12-19 Yuriy N. Bakhvalov