Tootfinder

Refactoring Codebases through Library Design
Ziga Kovacic, Celine Lee, Justin Chiu, Wenting Zhao, Kevin Ellis
https://arxiv.org/abs/2506.11058 https://

Refactoring Codebases through Library Design
Maintainable and general software allows developers to build robust applications efficiently, yet achieving these qualities often requires refactoring specialized solutions into reusable components. This challenge becomes particularly relevant as code agents become increasingly accurate at solving isolated programming problems. We investigate code agents' capacity to refactor code in ways supporting growth and reusability. We present both a method and a benchmark for refactoring: Librarian, a s…

An Efficient Augmented Lagrangian Method for Dynamic Optimal Transport on Surfaces Based on Second-Order Cone Programming
Liang Chen, Youyicun Lin, Yuxuan Zhou
https://arxiv.org/abs/2506.08988

An Efficient Augmented Lagrangian Method for Dynamic Optimal Transport on Surfaces Based on Second-Order Cone Programming
This paper proposes an efficient numerical optimization approach for solving dynamic optimal transport (DOT) problems on general smooth surfaces, computing both the quadratic Wasserstein distance and the associated transportation path. Building on the convex DOT model of Benamou and Brenier, we first properly reformulate its dual problem, discretized on a triangular mesh for space together with a staggered grid for time, to a linear second-order cone programming. Then the resulting finite-dimen…

This https://arxiv.org/abs/2410.00117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

An Overview of the Burer-Monteiro Method for Certifiable Robot Perception
This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time. BM is often used to solve semidefinite programming relaxations, which can be used to perform global optimization for non-convex perception problems. Specifically, BM leverages the low-rank structure of typical semidefinite programs to dramatically reduce the computational cost of performing optimization. This paper discus…

When your function expects a path fragment string as a parameter, you MUST specify, explicitly, if it should have a leading, trailing, or neither forward slash.
If you do not say *explicitly in the docblock of that exact function/method*, then you are wrong and your code is wrong and I will curse your name every time I am forced to use your broken code.
#Programming

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Stefan Dascalescu, Adrian Marius Dumitran, Mihai Alexandru Vasiluta
https://arxiv.org/abs/2506.05990

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Competitive programming contests play a crucial role in cultivating computational thinking and algorithmic skills among learners. However, generating comprehensive test cases to effectively assess programming solutions remains resource-intensive and challenging for educators. This paper introduces an innovative NLP-driven method leveraging generative AI (large language models) to automate the creation of high-quality test cases for competitive programming assessments. We extensively evaluated o…

Sum Rate Maximization for Pinching Antennas Assisted RSMA System With Multiple Waveguides
Peiyu Wang, Hong Wang, Rongfang Song
https://arxiv.org/abs/2506.10596

Sum Rate Maximization for Pinching Antennas Assisted RSMA System With Multiple Waveguides
In this letter, a pinching antennas (PAs) assisted rate splitting multiple access (RSMA) system with multiple waveguides is investigated to maximize sum rate. A two-step algorithm is proposed to determine PA activation scheme and optimize the waveguide beamforming. Specifically, a low complexity spatial correlation and distance based method is proposed for PA activation selection. After determining the PA activation status, a semi-definite programming (SDP) based successive convex approximation…

A Newton Augmented Lagrangian Method for Symmetric Cone Programming with Complexity Analysis
Rui-Jin Zhang, Ruoyu Diao, Xin-Wei Liu, Yu-Hong Dai
https://arxiv.org/abs/2506.04802

A Newton Augmented Lagrangian Method for Symmetric Cone Programming with Complexity Analysis
Symmetric cone programming incorporates a broad class of convex optimization problems, including linear programming, second-order cone programming, and semidefinite programming. Although the augmented Lagrangian method (ALM) is well-suited for large-scale scenarios, its subproblems are often not second-order continuously differentiable, preventing direct use of classical Newton methods. To address this issue, we observe that barrier functions from interior-point methods (IPMs) naturally serve a…

Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka, Gene Cheung
https://arxiv.org/abs/2506.01826

Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed graph with no cycles containing an odd number of negative edges. Laplacian of a balanced signed graph has eigenvectors that map via a simple linear transform to ones in a corresponding positive graph Laplacian, thus enabling reuse of spectral filtering tools designed for positive graphs. We propose an efficient method t…

Stochastic Quadratic Dynamic Programming
Vincent Guigues, Adriana Washington
https://arxiv.org/abs/2506.07314 https://arxiv.org/pdf/2…

Stochastic Quadratic Dynamic Programming
We introduce an algorithm called SQDP (Stochastic Quadratic Dynamic Programming) to solve some multistage stochastic optimization problems having strongly convex recourse functions. The algorithm extends the classical Stochastic Dual Dynamic Programming (SDDP) method replacing affine cuts by quadratic cuts. We provide conditions ensuring strong convexity of the recourse functions and prove the convergence of SQDP. In the special case of a single stage deterministic problem, we call QCSC (Quadra…

Symbolically Regressing Fish Biomass Spectral Data: A Linear Genetic Programming Method with Tunable Primitives
Zhixing Huang, Bing Xue, Mengjie Zhang, Jeremy S. Ronney, Keith C. Gordon, Daniel P. Killeen
https://arxiv.org/abs/2505.21901

Symbolically Regressing Fish Biomass Spectral Data: A Linear Genetic Programming Method with Tunable Primitives
Machine learning techniques play an important role in analyzing spectral data. The spectral data of fish biomass is useful in fish production, as it carries many important chemistry properties of fish meat. However, it is challenging for existing machine learning techniques to comprehensively discover hidden patterns from fish biomass spectral data since the spectral data often have a lot of noises while the training data are quite limited. To better analyze fish biomass spectral data, this pap…

This https://arxiv.org/abs/2202.13250 has been replaced.
link: https://scholar.google.com/scholar?q=a

TabID: Automatic Identification and Tabulation of Subproblems in Constraint Models
The performance of a constraint model can often be improved by converting a subproblem into a single table constraint (referred to as tabulation). Finding subproblems to tabulate is traditionally a manual and time-intensive process, even for expert modellers. This paper presents TabID, an entirely automated method to identify promising subproblems for tabulation in constraint programming. We introduce a diverse set of heuristics designed to identify promising candidates for tabulation, aiming t…

Leveraging machine learning features for linear optical interferometer control
Sergei S. Kuzmin, Ivan V. Dyakonov, Stanislav S. Straupe
https://arxiv.org/abs/2505.24032

Leveraging machine learning features for linear optical interferometer control
We have developed an algorithm that constructs a model of a reconfigurable optical interferometer, independent of specific architectural constraints. The programming of unitary transformations on the interferometer's optical modes relies on either an analytical method for deriving the unitary matrix from a set of phase shifts or an optimization routine when such decomposition is not available. Our algorithm employs a supervised learning approach, aligning the interferometer model with a trainin…

IAE Optimized PID Tuning with Phase Margin and Crossover Frequency Constraints
Senol Gulgonul
https://arxiv.org/abs/2506.00923 https://

IAE Optimized PID Tuning with Phase Margin and Crossover Frequency Constraints
This paper presents PMwc-Tune, a novel PID tuning method that uniquely combines frequency-domain robustness constraints with time-domain performance optimization through constrained nonlinear programming. The key contribution is a unified formulation that simultaneously enforces phase margin and crossover frequency requirements (via nonlinear equality constraints) while minimizing the Integral Absolute Error (IAE) of the closed-loop response. The algorithm employs Sequential Quadratic Programmi…

An Overview of GPU-based First-Order Methods for Linear Programming and Extensions
Haihao Lu, Jinwen Yang
https://arxiv.org/abs/2506.02174 https://

An Overview of GPU-based First-Order Methods for Linear Programming and Extensions
The rapid progress in GPU computing has revolutionized many fields, yet its potential in mathematical programming, such as linear programming (LP), has only recently begun to be realized. This survey aims to provide a comprehensive overview of recent advancements in GPU-based first-order methods for LP, with a particular focus on the design and development of cuPDLP. We begin by presenting the design principles and algorithmic foundation of the primal-dual hybrid gradient (PDHG) method, which f…

Symbolically Regressing Fish Biomass Spectral Data: A Linear Genetic Programming Method with Tunable Primitives
Zhixing Huang, Bing Xue, Mengjie Zhang, Jeremy S. Ronney, Keith C. Gordon, Daniel P. Killeen
https://arxiv.org/abs/2505.21901

Symbolically Regressing Fish Biomass Spectral Data: A Linear Genetic Programming Method with Tunable Primitives
Machine learning techniques play an important role in analyzing spectral data. The spectral data of fish biomass is useful in fish production, as it carries many important chemistry properties of fish meat. However, it is challenging for existing machine learning techniques to comprehensively discover hidden patterns from fish biomass spectral data since the spectral data often have a lot of noises while the training data are quite limited. To better analyze fish biomass spectral data, this pap…

This https://arxiv.org/abs/2506.03561 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Disjunctive Benders Decomposition
We propose a novel enhancement to Benders Decomposition (BD) that generates valid inequalities for the convex hull of the Benders reformulation, addressing a key limitation of conventional BD-its cuts are typically tight only for the continuous relaxation. Our method efficiently integrates disjunctive programming theory with BD, introducing a new routine that leverages existing cut-generating oracles for uncovering constraints required to construct valid inequalities for the convex hull. For mi…

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 version of geometric programming problem consisting in minimizing a function given by the maximum of finitely many log-Laplace transforms of discrete nonnegative measures on a Euclidean space. Under a coerciveness assumption, we show that an $\varepsilon$-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…

Trilevel Memetic Algorithm for the Electric Vehicle Routing Problem
Ivan Milinovi\'c, Leon Stjepan Uroi\'c, Marko {\DJ}urasevi\'c
https://arxiv.org/abs/2506.01065

Trilevel Memetic Algorithm for the Electric Vehicle Routing Problem
The Electric Vehicle Routing Problem (EVRP) extends the capacitated vehicle routing problem by incorporating battery constraints and charging stations, posing significant optimization challenges. This paper introduces a Trilevel Memetic Algorithm (TMA) that hierarchically optimizes customer sequences, route assignments, and charging station insertions. The method combines genetic algorithms with dynamic programming, ensuring efficient and high-quality solutions. Benchmark tests on WCCI2020 inst…

Boundary bilinear control of semilinear parabolic PDEs: quadratic convergence of the SQP method
Eduardo Casas, Mariano Mateos
https://arxiv.org/abs/2505.24237

Boundary bilinear control of semilinear parabolic PDEs: quadratic convergence of the SQP method
We analyze a bilinear control problem governed by a semilinear parabolic equation. The control variable is the Robin coefficient on the boundary. First-order necessary and second-order sufficient optimality conditions are derived. A sequential quadratic programming algorithm is then proposed to compute local solutions. Starting the iterations from an initial point in an $L^2$-neighborhood of the local solution we prove stability and quadratic convergence of the algorithm in $L^p$ ($p < \infty$)…