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Combinatorial optimization is considered a promising class of problems in which quantum computers can show significant advantages. However, problems of practical relevance typically have more variables than current or foreseeable quantum…

Quantum Physics · Physics 2025-12-23 Mathias Schmid , Naeimeh Mohseni , Michael J. Hartmann

Cylindrical algebraic decomposition (CAD) is an important tool, both for quantifier elimination over the reals and a range of other applications. Traditionally, a CAD is built through a process of projection and lifting to move the problem…

Symbolic Computation · Computer Science 2014-08-28 Matthew England , David Wilson , Russell Bradford , James H. Davenport

Cylindrical Algebraic Decompositions (CADs) endowed with additional topological properties have found applications beyond their original logical setting, including algorithmic optimizations in CAD construction, robot motion planning, and…

Algebraic Geometry · Mathematics 2026-01-16 Lucas Michel

In computer science, divide and conquer (D&C) is an algorithm design paradigm based on multi-branched recursion. A D&C algorithm works by recursively and monotonically breaking down a problem into sub problems of the same (or a related)…

Computation and Language · Computer Science 2018-09-24 Diego Gabriel Krivochen

Cylindrical algebraic decomposition (CAD) is an important tool for the investigation of semi-algebraic sets, with applications within algebraic geometry and beyond. We recently reported on a new implementation of CAD in Maple which…

Symbolic Computation · Computer Science 2013-06-14 Matthew England

Gr\"obner Bases and Cylindrical Algebraic Decomposition are generally thought of as two, rather different, methods of looking at systems of equations and, in the case of Cylindrical Algebraic Decomposition, inequalities. However, even for a…

Symbolic Computation · Computer Science 2012-07-30 David J. Wilson , Russell J. Bradford , James H. Davenport

We propose a distributed computing framework, based on a divide and conquer strategy and hierarchical modeling, to accelerate posterior inference for high-dimensional Bayesian factor models. Our approach distributes the task of…

Methodology · Statistics 2016-12-30 Gautam Sabnis , Debdeep Pati , Barbara Engelhardt , Natesh Pillai

We discuss issues of problem formulation for algorithms in real algebraic geometry, focussing on quantifier elimination by cylindrical algebraic decomposition. We recall how the variable ordering used can have a profound effect on both…

Symbolic Computation · Computer Science 2014-06-26 Matthew England

We present a novel certified and complete algorithm to compute arrangements of real planar algebraic curves. It provides a geometric-topological analysis of the decomposition of the plane induced by a finite number of algebraic curves in…

Computational Geometry · Computer Science 2012-01-13 Eric Berberich , Pavel Emeliyanenko , Alexander Kobel , Michael Sagraloff

Cylindrical Algebraic Decomposition (CAD) was the first practical means for doing real quantifier elimination (QE), and is still a major method, with many improvements since Collins' original method. Nevertheless, its complexity is…

Symbolic Computation · Computer Science 2023-12-08 James H. Davenport , Zak P. Tonks , Ali K. Uncu

In this paper, we present a generalized Cuppen's divide-and-conquer algorithm for the symmetric tridiagonal eigenproblem. We extend the Cuppen's work to the rank two modifications of the form $A =T +\beta_1\bw_1\bw_1^T +…

Numerical Analysis · Mathematics 2015-06-30 Do Young Kwak , Jaeyeon Kim

We extensively describe our recently established "divide-and-conquer" semiclassical method [M. Ceotto, G. Di Liberto and R. Conte, Phys. Rev. Lett. 119, 010401 (2017)] and propose a new implementation of it to increase the accuracy of…

Chemical Physics · Physics 2018-01-15 Giovanni Di Liberto , Riccardo Conte , Michele Ceotto

We propose new algorithms for computing triangular decompositions of polynomial systems incrementally. With respect to previous works, our improvements are based on a {\em weakened} notion of a polynomial GCD modulo a regular chain, which…

Symbolic Computation · Computer Science 2011-04-06 Changbo Chen , Marc Moreno Maza

We consider the problem of decomposing a multivariate polynomial as the difference of two convex polynomials. We introduce algebraic techniques which reduce this task to linear, second order cone, and semidefinite programming. This allows…

Optimization and Control · Mathematics 2018-09-13 Amir Ali Ahmadi , Georgina Hall

Divide and Conquer (DC) is conceptually well suited to high-dimensional optimization by decomposing a problem into multiple small-scale sub-problems. However, appealing performance can be seldom observed when the sub-problems are…

Artificial Intelligence · Computer Science 2018-07-12 Peng Yang , Ke Tang , Xin Yao

Divide and Conquer is a well known algorithmic procedure for solving many kinds of problem. In this procedure, the problem is partitioned into two parts until the problem is trivially solvable. Finding the distance of the closest pair is an…

Computational Geometry · Computer Science 2011-11-11 Mohammad Zaidul Karim , Nargis Akter

Let $\mathrm{R}$ be a real closed field, and $\mathrm{D} \subset \mathrm{R}$ an ordered domain. We describe an algorithm that given as input a polynomial $P \in \mathrm{D} [ X_{1},\ldots,X_{k} ]$, and a finite set, $\mathcal{A}= \{ p_{1},…

Algebraic Geometry · Mathematics 2016-10-11 Saugata Basu , Marie-Francoise Roy

Divide and Conquer (D&C) is a widely used algorithmic strategy for symmetric eigenvalue decomposition. Its natural parallelism makes D&C attractive on modern multicore CPUs and GPUs, but existing eigenvalue-only routines often default to…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-05-27 Ruiyi Zhan , Shaoshuai Zhang

When building a cylindrical algebraic decomposition (CAD) savings can be made in the presence of an equational constraint (EC): an equation logically implied by a formula. The present paper is concerned with how to use multiple ECs,…

Symbolic Computation · Computer Science 2015-07-20 Matthew England , Russell Bradford , James H. Davenport

Current quantum computers can only solve optimization problems of a very limited size. For larger problems, decomposition methods are required in which the original problem is broken down into several smaller sub-problems. These are then…

Optimization and Control · Mathematics 2025-04-30 Zongji Li , Tobias Seidel , Michael Bortz , Raoul Heese