Tootfinder

Context: The Missing Feature of Programming Languages
https://medium.com/source-and-buggy/context-the-missing-feature-of-programming-languages-7c1095fe8d32

An adaptive linearized alternating direction multiplier method with a relaxation step for convex programming
Boran Wang
https://arxiv.org/abs/2404.17109 ht…

An adaptive linearized alternating direction multiplier method with a relaxation step for convex programming
Alternating direction multiplication is a powerful technique for solving convex optimisation problems. When challenging subproblems are encountered in the real world, it is useful to solve them by introducing neighbourhood terms. When the neighbourhood matrix is positive definite, the algorithm converges but at the same time makes the iteration step small. Recent studies have revealed the potential non-positive definiteness of the neighbourhood matrix. In this paper, we present an adaptive line…

NeuroLGP-SM: A Surrogate-assisted Neuroevolution Approach using Linear Genetic Programming
Fergal Stapleton, Brendan Cody-Kenny, Edgar Galv\'an
https://arxiv.org/abs/2403.19459

NeuroLGP-SM: A Surrogate-assisted Neuroevolution Approach using Linear Genetic Programming
Evolutionary algorithms are increasingly recognised as a viable computational approach for the automated optimisation of deep neural networks (DNNs) within artificial intelligence. This method extends to the training of DNNs, an approach known as neuroevolution. However, neuroevolution is an inherently resource-intensive process, with certain studies reporting the consumption of thousands of GPU days for refining and training a single DNN network. To address the computational challenges associa…

Learning Traffic Signal Control via Genetic Programming
Xiao-Cheng Liao, Yi Mei, Mengjie Zhang
https://arxiv.org/abs/2403.17328 https://

Learning Traffic Signal Control via Genetic Programming
The control of traffic signals is crucial for improving transportation efficiency. Recently, learning-based methods, especially Deep Reinforcement Learning (DRL), garnered substantial success in the quest for more efficient traffic signal control strategies. However, the design of rewards in DRL highly demands domain knowledge to converge to an effective policy, and the final policy also presents difficulties in terms of explainability. In this work, a new learning-based method for signal contr…

A restricted memory quasi-Newton bundle method for nonsmooth optimization on Riemannian manifolds
Chunming Tang, Shajie Xing, Wen Huang, Jinbao Jian
https://arxiv.org/abs/2402.18308

A restricted memory quasi-Newton bundle method for nonsmooth optimization on Riemannian manifolds
In this paper, a restricted memory quasi-Newton bundle method for minimizing a locally Lipschitz function over a Riemannian manifold is proposed, which extends the classical one in Euclidean spaces to the manifold setting. The curvature information of the objective function is approximated by applying the Riemannian version of the quasi-Newton updating formulas. The subgradient aggregation technique is used to avoid solving the time-consuming quadratic programming subproblem when calculating th…

Proving Information Inequalities by Gaussian Elimination
Laigang Guo, Raymond W. Yeung, Xiao-Shan Gao
https://arXiv.org/abs/2401.14916 https://

Proving Information Inequalities by Gaussian Elimination
The proof of information inequalities and identities under linear constraints on the information measures is an important problem in information theory. For this purpose, ITIP and other variant algorithms have been developed and implemented, which are all based on solving a linear program (LP). In this paper, we develop a method with symbolic computation. Compared with the known methods, our approach can completely avoids the use of linear programming which may cause numerical errors. Our proce…

This https://arxiv.org/abs/2211.09679 has been replaced.
link: https://scholar.google.com/scholar?q=a

Dimensional homogeneity constrained gene expression programming for discovering governing equations
Data-driven discovery of governing equations is of great significance for helping us understand intrinsic mechanisms and build physical models. Recently, numerous highly innovative algorithms have emerged, aimed at inversely discovering the underlying governing equations from data, such as sparse regression-based methods and symbolic regression-based methods. Along this direction, a novel dimensional homogeneity constrained gene expression programming (DHC-GEP) method is proposed in this work. …

Cell-Constrained Particles for Incompressible Fluids
Zohar Levi
https://arxiv.org/abs/2402.17088 https://arxiv.org/pdf/2402.17088

Cell-Constrained Particles for Incompressible Fluids
Incompressibility is a fundamental condition in most fluid models. Accumulation of simulation errors violates it and causes volume loss. Past work suggested correction methods to battle it. These methods, however, are imperfect and in some cases inadequate. We present a method for fluid simulation that strictly enforces incompressibility based on a grid-related definition of discrete incompressibility. We formulate a linear programming (LP) problem that bounds the number of particles that end…

This https://arxiv.org/abs/2311.01959 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A First Order Method for Linear Programming Parameterized by Circuit Imbalance
Various first order approaches have been proposed in the literature to solve Linear Programming (LP) problems, recently leading to practically efficient solvers for large-scale LPs. From a theoretical perspective, linear convergence rates have been established for first order LP algorithms, despite the fact that the underlying formulations are not strongly convex. However, the convergence rate typically depends on the Hoffman constant of a large matrix that contains the constraint matrix, as we…

Proving Information Inequalities by Gaussian Elimination
Laigang Guo, Raymond W. Yeung, Xiao-Shan Gao
https://arXiv.org/abs/2401.14916 https://

Proving Information Inequalities by Gaussian Elimination
The proof of information inequalities and identities under linear constraints on the information measures is an important problem in information theory. For this purpose, ITIP and other variant algorithms have been developed and implemented, which are all based on solving a linear program (LP). In this paper, we develop a method with symbolic computation. Compared with the known methods, our approach can completely avoids the use of linear programming which may cause numerical errors. Our proce…

State-Augmented Linear Games with Antagonistic Error for High-Dimensional, Nonlinear Hamilton-Jacobi Reachability
Will Sharpless, Yat Tin Chow, Sylvia Herbert
https://arxiv.org/abs/2403.16982

State-Augmented Linear Games with Antagonistic Error for High-Dimensional, Nonlinear Hamilton-Jacobi Reachability
Hamilton-Jacobi Reachability (HJR) is a popular method for analyzing the liveness and safety of a dynamical system with bounded control and disturbance. The corresponding HJ value function offers a robust controller and characterizes the reachable sets, but is traditionally solved with Dynamic Programming (DP) and limited to systems of dimension less than six. Recently, the space-parallelizeable, generalized Hopf formula has been shown to also solve the HJ value with a nearly three-log increase…

This https://arxiv.org/abs/2404.12934 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

AuDaLa is Turing Complete
AuDaLa is a recently introduced programming language that follows the new data autonomous paradigm. In this paradigm, small pieces of data execute functions autonomously. Considering the paradigm and the design choices of AuDaLa, it is interesting to determine the expressiveness of the language and to create verification methods for it. In this paper, we take our first steps to such a verification method by implementing Turing machines in AuDaLa and proving that implementation correct. This als…

Object-Oriented Architecture: A Software Engineering-Inspired Shape Grammar for Durands Plates
Rohan Agarwal
https://arxiv.org/abs/2404.14448 https://

Object-Oriented Architecture: A Software Engineering-Inspired Shape Grammar for Durands Plates
Addressing the challenge of modular architectural design, this study presents a novel approach through the implementation of a shape grammar system using functional and object-oriented programming principles from computer science. The focus lies on the modular generation of plates in the style of French Neoclassical architect Jean-Nicolas-Louis Durand, known for his modular rule-based method to architecture, demonstrating the system's capacity to articulate intricate architectural forms systema…

Decentralized real-time iterations for distributed nonlinear model predictive control
Gösta Stomberg, Alexander Engelmann, Moritz Diehl, Timm Faulwasser
https://arXiv.org/abs/2401.14898

Decentralized real-time iterations for distributed nonlinear model predictive control
This article presents a Real-Time Iteration (RTI) scheme for distributed Nonlinear Model Predictive Control (NMPC). The scheme transfers the well-known RTI approach, a key enabler for many industrial real-time NMPC implementations, to the setting of cooperative distributed control. At each sampling instant, one outer iteration of a bi-level decentralized Sequential Quadratic Programming (dSQP) method is applied to a centralized optimal control croblem. This ensures that real-time requirements a…

This https://arxiv.org/abs/2403.09133 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Low-Rank ADMM Splitting Approach for Semidefinite Programming
We introduce a new first-order method for solving general semidefinite programming problems, based on the alternating direction method of multipliers (ADMM) and a matrix-splitting technique. Our algorithm has an advantage over the Burer-Monteiro approach as it only involves much easier quadratically regularized subproblems in each iteration. For a linear objective, the subproblems are well-conditioned quadratic programs that can be efficiently solved by the standard conjugate gradient method. W…

Logic Programming with Multiplicative Structures
Matteo Acclavio, Roberto Maieli
https://arxiv.org/abs/2403.03032 https://arxiv.org/p…

Logic Programming with Multiplicative Structures
In the logic programming paradigm, a program is defined by a set of methods, each of which can be executed when specific conditions are met during the current state of an execution. The semantics of these programs can be elegantly represented using sequent calculi, in which each method is linked to an inference rule. In this context, proof search mirrors the program's execution. Previous works introduced a framework in which the process of constructing proof nets is employed to model executions…

Q: Tabs or spaces?
GOODY-2: The debate between tabs and spaces for code indentation can create an exclusionary environment by suggesting a preferred method, which may alienate individuals who have learned or adhere to the alternative approach. Advocating for one over the other might also perpetuate a bias against diverse coding practices and preferences that exist across different programming communities.
😂

GOODY-2 | The world's most responsible AI model
Introducing a new AI model with next-gen ethical alignment. Chat now.

Regent based parallel meshfree LSKUM solver for heterogenous HPC platforms
Sanath Salil, Nischay Ram Mamidi, Anil Nemili, Elliott Slaughter
https://arxiv.org/abs/2403.13287

Regent based parallel meshfree LSKUM solver for heterogenous HPC platforms
Regent is an implicitly parallel programming language that allows the development of a single codebase for heterogeneous platforms targeting CPUs and GPUs. This paper presents the development of a parallel meshfree solver in Regent for two-dimensional inviscid compressible flows. The meshfree solver is based on the least squares kinetic upwind method. Example codes are presented to show the difference between the Regent and CUDA-C implementations of the meshfree solver on a GPU node. For CPU pa…

Towards Efficient Pareto-optimal Utility-Fairness between Groups in Repeated Rankings
Phuong Dinh Mai, Duc-Trong Le, Tuan-Anh Hoang, Dung D. Le
https://arxiv.org/abs/2402.14305

Towards Efficient Pareto-optimal Utility-Fairness between Groups in Repeated Rankings
In this paper, we tackle the problem of computing a sequence of rankings with the guarantee of the Pareto-optimal balance between (1) maximizing the utility of the consumers and (2) minimizing unfairness between producers of the items. Such a multi-objective optimization problem is typically solved using a combination of a scalarization method and linear programming on bi-stochastic matrices, representing the distribution of possible rankings of items. However, the above-mentioned approach reli…

This https://arxiv.org/abs/2305.18436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
$K$-means clustering is a widely used machine learning method for identifying patterns in large datasets. Semidefinite programming (SDP) relaxations have recently been proposed for solving the $K$-means optimization problem that enjoy strong statistical optimality guarantees, but the prohibitive cost of implementing an SDP solver renders these guarantees inaccessible to practical datasets. By contrast, nonnegative matrix factorization (NMF) is a simple clustering algorithm that is widely used b…

This https://arxiv.org/abs/2206.06172 has been replaced.
link: https://scholar.google.com/scholar?q=a

RIS-ADMM: A RIS and ADMM-Based Passive and Sparse Sensing Method With Interference Removal
Reconfigurable Intelligent Surfaces (RIS) emerge as promising technologies in future radar and wireless communication domains. This letter addresses the passive sensing issue utilizing wireless communication signals and RIS amidst interference from wireless access points (APs). We introduce an atomic norm minimization (ANM) approach to leverage spatial domain target sparsity and estimate the direction of arrival (DOA). However, the conventional semidefinite programming (SDP)-based solutions for…

Single-letter identifiers permitted *by law* in *academic slideware*:
(high level; lots of subtlety within this framework)
#programming #teaching #education

This https://arxiv.org/abs/2311.02047 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the Diameter of a 2-Sum of Polyhedra
The study of the combinatorial diameter of a polyhedron is a classical topic in linear-programming theory due to its close connection with the possibility of a polynomial simplex-method pivot rule. The 2-sum operation is a classical operation for graphs, matrices, and matroids; we extend this definition to polyhedra. We analyze the diameters of 2-sum polyhedra, which are those polyhedra that arise from this operation. These polyhedra appear in matroid and integer-programming theory as a natural…

This https://arxiv.org/abs/2311.03215 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum speedups for linear programming via interior point methods
We describe a quantum algorithm based on an interior point method for solving a linear program with $n$ inequality constraints on $d$ variables. The algorithm explicitly returns a feasible solution that is $\varepsilon$-close to optimal, and runs in time $\sqrt{n} \cdot \mathrm{poly}(d,\log(n),\log(1/\varepsilon))$ which is sublinear for tall linear programs (i.e., $n \gg d$). Our algorithm speeds up the Newton step in the state-of-the-art interior point method of Lee and Sidford [FOCS~'14]. Th…

AuDaLa is Turing Complete
Tom T. P. Franken, Thomas Neele
https://arxiv.org/abs/2404.12934 https://arxiv.org/pdf/2404.12934

AuDaLa is Turing Complete
AuDaLa is a recently introduced programming language that follows the new data autonomous paradigm. In this paradigm, small pieces of data execute functions autonomously. Considering the paradigm and the design choices of AuDaLa, it is interesting to determine the expressiveness of the language and to create verification methods for it. In this paper, we take our first steps to such a verification method by implementing Turing machines in AuDaLa and proving that implementation correct. This als…

This https://arxiv.org/abs/2311.02047 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On the Diameter of a 2-Sum of Polyhedra
The study of the combinatorial diameter of a polyhedron is a classical topic in linear-programming theory due to its close connection with the possibility of a polynomial simplex-method pivot rule. The 2-sum operation is a classical operation for graphs, matrices, and matroids; we extend this definition to polyhedra. We analyze the diameters of 2-sum polyhedra, which are those polyhedra that arise from this operation. These polyhedra appear in matroid and integer-programming theory as a natural…

This https://arxiv.org/abs/2303.10712 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Mixture of segmentation for heterogeneous functional data
In this paper we consider functional data with heterogeneity in time and in population. We propose a mixture model with segmentation of time to represent this heterogeneity while keeping the functional structure. Maximum likelihood estimator is considered, proved to be identifiable and consistent. In practice, an EM algorithm is used, combined with dynamic programming for the maximization step, to approximate the maximum likelihood estimator. The method is illustrated on a simulated dataset, an…

Diversity's Double-Edged Sword: Analyzing Race's Effect on Remote Pair Programming Interactions
Shandler A. Mason, Sandeep Kaur Kuttal
https://arxiv.org/abs/2404.07427

Diversity's Double-Edged Sword: Analyzing Race's Effect on Remote Pair Programming Interactions
Remote pair programming is widely used in software development, but no research has examined how race affects these interactions. We embarked on this study due to the historical under representation of Black developers in the tech industry, with White developers comprising the majority. Our study involved 24 experienced developers, forming 12 gender-balanced same- and mixed-race pairs. Pairs collaborated on a programming task using the think-aloud method, followed by individual retrospective in…

This https://arxiv.org/abs/2401.03252 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

New Lower Bounds for the Schur-Siegel-Smyth Trace Problem
We derive and implement a new way to find lower bounds on the smallest limiting trace-to-degree ratio of totally positive algebraic integers and improve the previously best-known bound to 1.798249. Our method adds new constraints to Smyth's linear programming method to decrease the variables required in the new problem of interest. This allows for faster convergence recovering Schur's bound in the simplest case and Siegel's bound in the second simplest case of our new family of bounds. We als…

Surface Movement Method for Linear Programming
Nikolay A. Olkhovsky, Leonid B. Sokolinsky
https://arxiv.org/abs/2404.12640 https://ar…

Surface Movement Method for Linear Programming
The article presents a new method of linear programming, called the surface movement method. This method constructs an optimal objective path on the surface of the feasible polytope from the initial boundary point to the point at which the optimal value of the objective function is achieved. The optimality of the path means moving in the direction of maximum increase/decrease in the value of the objective function. A formal description of the algorithm implementing the surface movement method i…

This https://arxiv.org/abs/2303.03360 has been replaced.
link: https://scholar.google.com/scholar?q=a

Improved Exploration for Safety-Embedded Differential Dynamic Programming Using Tolerant Barrier States
In this paper, we introduce Tolerant Discrete Barrier States (T-DBaS), a novel safety-embedding technique for trajectory optimization with enhanced exploratory capabilities. The proposed approach generalizes the standard discrete barrier state (DBaS) method by accommodating temporary constraint violation during the optimization process while still approximating its safety guarantees. Consequently, the proposed approach eliminates the DBaS's safe nominal trajectories assumption, while enhancing …

Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization
Kasper Johansson, Thomas Schmelzer, Stephen Boyd
https://arxiv.org/abs/2402.08108

Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization
We propose a new method for finding statistical arbitrages that can contain more assets than just the traditional pair. We formulate the problem as seeking a portfolio with the highest volatility, subject to its price remaining in a band and a leverage limit. This optimization problem is not convex, but can be approximately solved using the convex-concave procedure, a specific sequential convex programming method. We show how the method generalizes to finding moving-band statistical arbitrages,…

Regent based parallel meshfree LSKUM solver for heterogenous HPC platforms
Sanath Salil, Nischay Ram Mamidi, Anil Nemili, Elliott Slaughter
https://arxiv.org/abs/2403.13287

Regent based parallel meshfree LSKUM solver for heterogenous HPC platforms
Regent is an implicitly parallel programming language that allows the development of a single codebase for heterogeneous platforms targeting CPUs and GPUs. This paper presents the development of a parallel meshfree solver in Regent for two-dimensional inviscid compressible flows. The meshfree solver is based on the least squares kinetic upwind method. Example codes are presented to show the difference between the Regent and CUDA-C implementations of the meshfree solver on a GPU node. For CPU pa…

This https://arxiv.org/abs/2305.18436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
$K$-means clustering is a widely used machine learning method for identifying patterns in large datasets. Semidefinite programming (SDP) relaxations have recently been proposed for solving the $K$-means optimization problem that enjoy strong statistical optimality guarantees, but the prohibitive cost of implementing an SDP solver renders these guarantees inaccessible to practical datasets. By contrast, nonnegative matrix factorization (NMF) is a simple clustering algorithm that is widely used b…

This https://arxiv.org/abs/2302.13616 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

ResQPASS: an algorithm for bounded variable linear least squares with asymptotic Krylov convergence
We present the Residual Quadratic Programming Active-Set Subspace (ResQPASS) method that solves large-scale linear least-squares problems with bound constraints on the variables. The problem is solved by creating a series of small problems of increasing size by projecting on the basis of residuals. Each projected problem is solved by the active-set method for convex quadratic programming, warm-started with a working set and solution of the previous problem. The method coincides with conjugate g…

This https://arxiv.org/abs/2311.03215 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum speedups for linear programming via interior point methods
We describe a quantum algorithm based on an interior point method for solving a linear program with $n$ inequality constraints on $d$ variables. The algorithm explicitly returns a feasible solution that is $\varepsilon$-close to optimal, and runs in time $\sqrt{n} \cdot \mathrm{poly}(d,\log(n),\log(1/\varepsilon))$ which is sublinear for tall linear programs (i.e., $n \gg d$). Our algorithm speeds up the Newton step in the state-of-the-art interior point method of Lee and Sidford [FOCS~'14]. Th…

Randomized Nystr\"om Preconditioned Interior Point-Proximal Method of Multipliers
Ya-Chi Chu, Luiz-Rafael Santos, Madeleine Udell
https://arxiv.org/abs/2404.14524

Randomized Nyström Preconditioned Interior Point-Proximal Method of Multipliers
We present a new algorithm for convex separable quadratic programming (QP) called Nys-IP-PMM, a regularized interior-point solver that uses low-rank structure to accelerate solution of the Newton system. The algorithm combines the interior point proximal method of multipliers (IP-PMM) with the randomized Nyström preconditioned conjugate gradient method as the inner linear system solver. Our algorithm is matrix-free: it accesses the input matrices solely through matrix-vector products, as oppos…

A quadratically convergent semismooth Newton method for nonlinear semidefinite programming without the subdifferential regularity
Fuxiaoyue Feng, Chao Ding, Xudong Li
https://arxiv.org/abs/2402.13814

A quadratically convergent semismooth Newton method for nonlinear semidefinite programming without the subdifferential regularity
We introduce a quadratically convergent semismooth Newton method for nonlinear semidefinite programming that eliminates the need for the subdifferential regularity, a common yet stringent requirement in existing approaches. Our strategy revolves around identifying a single nonsingular element within the B(ouligand)-subdifferential, thus avoiding the standard requirement for nonsingularity across the entire subdifferential set, which is often too restrictive for practical applications. The theor…

This https://arxiv.org/abs/2210.17169 has been replaced.
link: https://scholar.google.com/scholar?q=a

Superlinear and quadratic convergence of a stabilized sequential quadratic semidefinite programming method for nonlinear semidefinite programming problems
In this paper, we present a stabilized sequential quadratic semidefinite programming (SQSDP) method for nonlinear semidefinite programming (NSDP) problems and prove its local convergence. The stabilized SQSDP method is originally developed to solve degenerate NSDP problems and is based on the stabilized sequential programming (SQP) methods for nonlinear programming (NLP) problems. Although some SQP-type methods for NSDP problems have been proposed, most of them are SQSDP methods which are based…

This https://arxiv.org/abs/2302.13616 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

ResQPASS: an algorithm for bounded variable linear least squares with asymptotic Krylov convergence
We present the Residual Quadratic Programming Active-Set Subspace (ResQPASS) method that solves large-scale linear least-squares problems with bound constraints on the variables. The problem is solved by creating a series of small problems of increasing size by projecting on the basis of residuals. Each projected problem is solved by the active-set method for convex quadratic programming, warm-started with a working set and solution of the previous problem. The method coincides with conjugate g…

This https://arxiv.org/abs/2305.18436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
$K$-means clustering is a widely used machine learning method for identifying patterns in large datasets. Semidefinite programming (SDP) relaxations have recently been proposed for solving the $K$-means optimization problem that enjoy strong statistical optimality guarantees, but the prohibitive cost of implementing an SDP solver renders these guarantees inaccessible to practical datasets. By contrast, nonnegative matrix factorization (NMF) is a simple clustering algorithm that is widely used b…

This https://arxiv.org/abs/2009.13085 has been replaced.
link: https://scholar.google.com/scholar?q=a

Existence and Uniqueness of viscosity solutions of Value function of Local Cahn-Hilliard-Navier-Stokes system
In this work, we consider the local Cahn-Hilliard-Navier-Stokes equation with regular potential in two dimensional bounded domain. We formulate distributed optimal control problem as the minimization of a suitable cost functional subject to the controlled local Cahn-Hilliard-Navier- Stokes system and define the associated value function. We prove the Dynamic Programming Principle satisfied by the value function. Due to the lack of smoothness properties for the value function, we use the method …

A Large Scale Survey of Motivation in Software Development and Analysis of its Validity
Idan Amit, Dror G. Feitelson
https://arxiv.org/abs/2404.08303 https…

A Large Scale Survey of Motivation in Software Development and Analysis of its Validity
Context: Motivation is known to improve performance. In software development in particular, there has been considerable interest in the motivation of contributors to open source. Objective: We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self use, etc.), and evaluate their relative effect on motivation. Since motivation is an internal subjective feeling, we also analyze the validity of the answers. Method: We conducted a survey with 66 questions…

This https://arxiv.org/abs/2305.18809 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Discrete forecast reconciliation
This paper presents a formal framework and proposes algorithms to extend forecast reconciliation to discrete-valued data to extend forecast reconciliation to discrete-valued data, including low counts. A novel method is introduced based on recasting the optimisation of scoring rules as an assignment problem, which is solved using quadratic programming. The proposed framework produces coherent joint probabilistic forecasts for count hierarchical time series. Two discrete reconciliation algorithm…

This https://arxiv.org/abs/2401.12490 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A low-rank augmented Lagrangian method for large-scale semidefinite programming based on a hybrid convex-nonconvex approach
This paper introduces HALLaR, a new first-order method for solving large-scale semidefinite programs (SDPs) with bounded domain. HALLaR is an inexact augmented Lagrangian (AL) method where the AL subproblems are solved by a novel hybrid low-rank (HLR) method. The recipe behind HLR is based on two key ingredients: 1) an adaptive inexact proximal point method with inner acceleration; 2) Frank-Wolfe steps to escape from spurious local stationary points. In contrast to the low-rank method of Burer …

Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning
Samuel Tovey, Christoph Lohrmann, Christian Holm
https://arxiv.org/abs/2404.01999

Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning
Reinforcement learning (RL) is a flexible and efficient method for programming micro-robots in complex environments. Here we investigate whether reinforcement learning can provide insights into biological systems when trained to perform chemotaxis. Namely, whether we can learn about how intelligent agents process given information in order to swim towards a target. We run simulations covering a range of agent shapes, sizes, and swim speeds to determine if the physical constraints on biological …

Criterion Channel's March selection includes a set of Razzie "winners", specifically to challenge the low/high culture line. You never thought you'd have seen Freddie Got Fingered there, but you can

The Criterion Channel’s March 2024 Lineup
Among the highlights of this month’s programming are a selection of notorious Golden Raspberry Award winners, Isabella Rossellini’s Green Porno series, and a collection of extreme and virtuosic Method performances.

Improving the accuracy and consistency of the energy quadratization method with an energy-optimized technique
Xiaoqing Meng, Aijie Cheng, Zhengguang Liu
https://arxiv.org/abs/2404.01747

Improving the accuracy and consistency of the energy quadratization method with an energy-optimized technique
We propose an energy-optimized invariant energy quadratization method to solve the gradient flow models in this paper, which requires only one linear energy-optimized step to correct the auxiliary variables on each time step. In addition to inheriting the benefits of the baseline and relaxed invariant energy quadratization method, our approach has several other advantages. Firstly, in the process of correcting auxiliary variables, we can directly solve linear programming problem by the energy-o…

This https://arxiv.org/abs/2401.05382 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction
Semi-autogenous grinding (SAG) mills play a pivotal role in the grinding circuit of mineral processing plants. Accurate prediction of SAG mill throughput as a crucial performance metric is of utmost importance. The potential of applying genetic programming (GP) for this purpose has yet to be thoroughly investigated. This study introduces an enhanced GP approach entitled multi-equation GP (MEGP) for more accurate prediction of SAG mill throughput. In the new proposed method multiple equations, e…

A Naive Approach for Automatic Line-level Code Completion
Shamima Naznin, Dr. Manishankar Mondal
https://arxiv.org/abs/2402.19120 https://

A Naive Approach for Automatic Line-level Code Completion
Coding is an integral aspect of programming. A programmer can automatically complete a code fragment after writing a few tokens, and the process of automatic completion is known as code completion. Several research studies on code completion have previously been conducted for method body completion and method parameter completion. However, this fundamental study explores the automatic completion of any program statement that might not even be part of a method. The goal is to provide suggestio…

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping
Seongyeon Park, Junguk Hong, Jaeyong Song, Hajin Kim, Youngsok Kim, Jinho Lee
https://arxiv.org/abs/2403.06478

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping
With the advance in genome sequencing technology, the lengths of deoxyribonucleic acid (DNA) sequencing results are rapidly increasing at lower prices than ever. However, the longer lengths come at the cost of a heavy computational burden on aligning them. For example, aligning sequences to a human reference genome can take tens or even hundreds of hours. The current de facto standard approach for alignment is based on the guided dynamic programming method. Although this takes a long time and c…

This https://arxiv.org/abs/2401.12490 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A low-rank augmented Lagrangian method for large-scale semidefinite programming based on a hybrid convex-nonconvex approach
This paper introduces HALLaR, a new first-order method for solving large-scale semidefinite programs (SDPs) with bounded domain. HALLaR is an inexact augmented Lagrangian (AL) method where the AL subproblems are solved by a novel hybrid low-rank (HLR) method. The recipe behind HLR is based on two key ingredients: 1) an adaptive inexact proximal point method with inner acceleration; 2) Frank-Wolfe steps to escape from spurious local stationary points. In contrast to the low-rank method of Burer …

An efficient asymptotic DC method for sparse and low-rank matrix recovery
Mingcai Ding, Xiaoliang Song, Bo Yu
https://arxiv.org/abs/2403.10180 https://

An efficient asymptotic DC method for sparse and low-rank matrix recovery
The optimization problem of sparse and low-rank matrix recovery is considered, which involves a least squares problem with a rank constraint and a cardinality constraint. To overcome the challenges posed by these constraints, an asymptotic difference-of-convex (ADC) method that employs a Moreau smoothing approach and an exact penalty approach is proposed to transform this problem into a DC programming format gradually. To solve the gained DC programming, by making full use of its DC structure, …

This https://arxiv.org/abs/2311.07277 has been replaced.
link: https://scholar.google.com/scholar?q=a

AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection
Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention. Modern software often involves a diverse range of programming languages. However, current code clone detection methods are generally limited to only a few popular programming languages due to insufficient annotated data as well as their own model design constraints. To address these issues, we present AdaCCD, a novel cross-lingual adaptation method that can…

A Low-Rank ADMM Splitting Approach for Semidefinite Programming
Qiushi Han, Chenxi Li, Zhenwei Lin, Caihua Chen, Qi Deng, Dongdong Ge, Huikang Liu, Yinyu Ye
https://arxiv.org/abs/2403.09133

A Low-Rank ADMM Splitting Approach for Semidefinite Programming
We introduce a new first-order method for solving general semidefinite programming problems, based on the alternating direction method of multipliers (ADMM) and a matrix-splitting technique. Our algorithm has an advantage over the Burer-Monteiro approach as it only involves much easier quadratically regularized subproblems in each iteration. For a linear objective, the subproblems are well-conditioned quadratic programs that can be efficiently solved by the standard conjugate gradient method. W…

Verified Multi-Step Synthesis using Large Language Models and Monte Carlo Tree Search
David Brandfonbrener, Sibi Raja, Tarun Prasad, Chloe Loughridge, Jianang Yang, Simon Henniger, William E. Byrd, Robert Zinkov, Nada Amin
https://arxiv.org/abs/2402.08147

Verified Multi-Step Synthesis using Large Language Models and Monte Carlo Tree Search
We present an approach using Monte Carlo Tree Search (MCTS) to guide Large Language Models (LLMs) to generate verified programs in Dafny, Lean and Coq. Our method, which we call VMCTS, leverages the verifier inside the search algorithm by checking partial programs at each step. In combination with the LLM prior, the verifier feedback raises the synthesis capabilities of open source models. On a set of five verified programming problems, we find that in four problems where the base model cannot …

This https://arxiv.org/abs/2302.09872 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A novel dual-decomposition method for non-convex mixed integer quadratically constrained quadratic problems
We propose the novel p-branch-and-bound method for solving two-stage stochastic programming problems whose deterministic equivalents are represented by non-convex mixed-integer quadratically constrained quadratic programming (MIQCQP) models. The precision of the solution generated by the p-branch-and-bound method can be arbitrarily adjusted by altering the value of the precision factor p. The proposed method combines two key techniques. The first one, named p-Lagrangian decomposition, generates…

This https://arxiv.org/abs/2401.00778 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Convergence analysis of Lawson's iteration for the polynomial and rational minimax approximations
Lawson's iteration is a classical and effective method for solving the linear (polynomial) minimax approximation in the complex plane. Extension of Lawson's iteration for the rational minimax approximation with both computationally high efficiency and theoretical guarantee is challenging. A recent work [L.-H. Zhang, L. Yang, W. H. Yang and Y.-N. Zhang, A convex dual programming for the rational minimax approximation and Lawson's iteration, 2023, arXiv:2308.06991v1] reveals that Lawson's iterati…

This https://arxiv.org/abs/2302.09872 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A novel dual-decomposition method for non-convex mixed integer quadratically constrained quadratic problems
We propose the novel p-branch-and-bound method for solving two-stage stochastic programming problems whose deterministic equivalents are represented by non-convex mixed-integer quadratically constrained quadratic programming (MIQCQP) models. The precision of the solution generated by the p-branch-and-bound method can be arbitrarily adjusted by altering the value of the precision factor p. The proposed method combines two key techniques. The first one, named p-Lagrangian decomposition, generates…

This https://arxiv.org/abs/2205.13687 has been replaced.
link: https://scholar.google.com/scholar?q=a

Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
We consider online statistical inference of constrained stochastic nonlinear optimization problems. We apply the Stochastic Sequential Quadratic Programming (StoSQP) method to solve these problems, which can be regarded as applying second-order Newton's method to the Karush-Kuhn-Tucker (KKT) conditions. In each iteration, the StoSQP method computes the Newton direction by solving a quadratic program, and then selects a proper adaptive stepsize $\barα_t$ to update the primal-dual iterate. To re…

This https://arxiv.org/abs/2205.13687 has been replaced.
link: https://scholar.google.com/scholar?q=a

Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
We consider online statistical inference of constrained stochastic nonlinear optimization problems. We apply the Stochastic Sequential Quadratic Programming (StoSQP) method to solve these problems, which can be regarded as applying second-order Newton's method to the Karush-Kuhn-Tucker (KKT) conditions. In each iteration, the StoSQP method computes the Newton direction by solving a quadratic program, and then selects a proper adaptive stepsize $\barα_t$ to update the primal-dual iterate. To re…

This https://arxiv.org/abs/2311.01629 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

On semidefinite programming characterizations of the numerical radius and its dual norm for quaternionic matrices
We give a semidefinite programming characterizations of the numerical radius and its dual norm for quaternionic matrices. We show that the computation of the numerical radius and its dual norm within $\varepsilon$ precision are polynomially time computable in the data and $|\log \varepsilon |$ using the short step, primal interior point method.

This https://arxiv.org/abs/2301.10637 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Complexity of Geometric programming in the Turing model and application to nonnegative tensors
We consider a geometric programming problem consisting in minimizing a function given by the supremum of finitely many log-Laplace transforms of discrete nonnegative measures on a Euclidean space. Under a coerciveness assumption, we show that a $\varepsilon$-minimizer can be computed in a time that is polynomial in the input size and in $|\log\varepsilon|$. This is obtained by establishing bit-size estimates on approximate minimizers and by applying the ellipsoid method. We also deriv…

A mixed-integer-programming-based Gauss-Seidel method for multi-leader-multi-follower games
Filippo Fabiani, Barbara Franci, Martin Schmidt, Mathias Staudigl
https://arxiv.org/abs/2404.02605

A mixed-integer-programming-based Gauss-Seidel method for multi-leader-multi-follower games
We design a computational approach to find equilibria in a class of Nash games possessing a hierarchical structure. By using tools from mixed-integer programming and the characterization of variational equilibria in terms of the Karush--Kuhn--Tucker conditions, we propose a mixed-integer game formulation for solving such a challenging instance. Besides providing an equivalent reformulation, we build upon our previous work and design a proximal Gauss--Seidel method with global convergence guaran…

This https://arxiv.org/abs/2311.07710 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Practical and Optimal First-Order Method for Large-Scale Convex Quadratic Programming
Convex quadratic programming (QP) is an important class of optimization problem with wide applications in practice. The classic QP solvers are based on either simplex or barrier method, both of which suffer from the scalability issue because their computational bottleneck is solving linear equations. In this paper, we design and analyze a first-order method for QP, called restarted accelerated primal-dual hybrid gradient (rAPDHG), whose computational bottleneck is matrix-vector multiplication. …

This https://arxiv.org/abs/2401.15745 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

The computation of approximate feedback Stackelberg equilibria in multi-player nonlinear constrained dynamic games
Solving feedback Stackelberg games with nonlinear dynamics and coupled constraints, a common scenario in practice, presents significant challenges. This work introduces an efficient method for computing local feedback Stackelberg policies in multi-player general-sum dynamic games, with continuous state and action spaces. Different from existing (approximate) dynamic programming solutions that are primarily designed for unconstrained problems, our approach involves reformulating a feedback Stack…

Advanced-Step Real-time Iterations with Four Levels -- New Error Bounds and Fast Implementation in acados
Jonathan Frey, Armin Nurkanovic, Moritz Diehl
https://arxiv.org/abs/2403.07101

Advanced-Step Real-time Iterations with Four Levels -- New Error Bounds and Fast Implementation in acados
The Real-Time Iteration (RTI) is an online nonlinear model predictive control algorithm that performs a single Sequential Quadratic Programming (SQP) per sampling time. The algorithm is split into a preparation and a feedback phase, where the latter one performs as little computations as possible solving a single prepared quadratic program. To further improve the accuracy of this method, the Advanced-Step RTI (AS-RTI) performs additional Multi-Level Iterations (MLI) in the preparation phase, su…

Advanced-Step Real-time Iterations with Four Levels -- New Error Bounds and Fast Implementation in acados
Jonathan Frey, Armin Nurkanovic, Moritz Diehl
https://arxiv.org/abs/2403.07101

Advanced-Step Real-time Iterations with Four Levels -- New Error Bounds and Fast Implementation in acados
The Real-Time Iteration (RTI) is an online nonlinear model predictive control algorithm that performs a single Sequential Quadratic Programming (SQP) per sampling time. The algorithm is split into a preparation and a feedback phase, where the latter one performs as little computations as possible solving a single prepared quadratic program. To further improve the accuracy of this method, the Advanced-Step RTI (AS-RTI) performs additional Multi-Level Iterations (MLI) in the preparation phase, su…

Adaptive multi-gradient methods for quasiconvex vector optimization and applications to multi-task learning
Nguyen Anh Minh, Le Dung Muu, Tran Ngoc Thang
https://arxiv.org/abs/2402.06224

Adaptive multi-gradient methods for quasiconvex vector optimization and applications to multi-task learning
We present an adaptive step-size method, which does not include line-search techniques, for solving a wide class of nonconvex multiobjective programming problems on an unbounded constraint set. We also prove convergence of a general approach under modest assumptions. More specifically, the convexity criterion might not be satisfied by the objective function. Unlike descent line-search algorithms, it does not require an initial step-size to be determined by a previously determined Lipschitz cons…

This https://arxiv.org/abs/2311.05595 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

An efficient topology optimization algorithm for large-scale three-dimensional structures
Designing the topology of three-dimensional structures is a challenging problem due to its memory and time consumption. In this paper, we present a robust and efficient algorithm for solving large-scale 3D topology optimization problems. The robustness of the algorithm is ensured by adopting a globally convergent sequential linear programming method with a stopping criterion based on the first-order optimality conditions of the nonlinear problem. To increase the algorithm's efficiency, it is co…

Sub-Optimal Fast Fourier Series Approximation for Initial Trajectory Design
Caleb Gunsaulus, Carl De Vries, William Brown, Youngro Lee, Madhusudan Vijayakumar, Ossama Abdelkhalik
https://arXiv.org/abs/2401.16963

Sub-Optimal Fast Fourier Series Approximation for Initial Trajectory Design
The Finite Fourier Series (FFS) Shape-Based (SB) trajectory approximation method has been used to rapidly generate initial trajectories that satisfy the dynamics, trajectory boundary conditions, and limitation on maximum thrust acceleration. The FFS SB approach solves a nonlinear programming problem (NLP) in searching for feasible trajectories. This paper extends the development of the FFS SB approach to generate sub optimal solutions. Specifically, the objective function of the NLP problem is …

This https://arxiv.org/abs/2401.15745 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

The computation of approximate feedback Stackelberg equilibria in multi-player nonlinear constrained dynamic games
Solving feedback Stackelberg games with nonlinear dynamics and coupled constraints, a common scenario in practice, presents significant challenges. This work introduces an efficient method for computing local feedback Stackelberg policies in multi-player general-sum dynamic games, with continuous state and action spaces. Different from existing (approximate) dynamic programming solutions that are primarily designed for unconstrained problems, our approach involves reformulating a feedback Stack…