Search Results for "problem a"

...It generates question-and-answer pairs across a wide range of mathematical topics typically found in school-level curricula, testing a model’s ability to reason about algebra, arithmetic, calculus, probability, and more. Each question is programmatically generated with structured templates to ensure clear logic and reproducibility. The dataset enables models to learn mathematical problem-solving through examples that involve both numeric and symbolic reasoning. Version 1.0 includes over 2 million examples per category, with training splits labeled as “easy,” “medium,” and “hard,” supporting curriculum-based learning strategies. The data can be accessed via PyPI or generated locally using provided Python scripts, with outputs formatted for direct use in training or evaluation pipelines.

...It is an ecosystem-level tool, which records the toolchain and other metadata of projects and allows the reconstruction and rebuilding of that exact environment later. Lean 4 undergoes rapid development, which can introduce backward-compatibility issues. This problem becomes more difficult as the versions accumulate over time. xLaDe is built to mitigate the practical issues of backward-compatibility problems and improve ecosystem-level tooling for the Lean 4 theorem prover. Instead of directly solving backward-compatibility issues by storing every version of Lean 4 and other tools or providing cross-version compatibility, xLaDe tries to store sufficient environment metadata such as toolchains, dependencies, and other context, and then recreates the environment for running Lean 4 projects upon request, also termed as experiments in xLaDe.

...Technical documentation and tutorials are available under the following link: https://digraph3.readthedocs.io/en/latest/ The tutorials introduce the main objects like digraphs, outranking digraphs and performance tableaux. There is also a tutorial provided on undirected graphs. Some tutorials are problem oriented and show how to compute the winner of an election, how to build a best choice recommendation, or how to linearly rank or rate with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing maximal independent sets (MISs) and kernels in graphs and digraphs.

...Created in 2011 and maintained by Renato Alexandre dos Santos Freitas, ATC is designed as a practical Windows desktop application for advanced calculations, automation, and technical problem solving. ATC supports equation solving, polynomial tools, complex numbers, matrix calculations, statistics, physics, geometry, unit conversions, DSP/FFT operations, and scripting. It can also process batch TXT files, making it useful for repeatable calculations and automated workflows. The project also includes ATC Online Alpha, a WebAssembly-based version that brings parts of the ATC engine to the web. ...

A linear transformation is a function from one vector space to another that respects the linear structure of each vector space. For visualizing this we can transform a vector, but it has an obvious problem, because the linear transformation transform the whole space, not just the vector, in other words it transforms the basis vectors thus every vector in the space gets transformed too. That's why i created this app so everyone can see how a transformation matrix change the whole space.

The main goal of this project was developing phase-field simulations of lithium dendrite growth with FEniCS programmed in Python. The problem was based in the grand potential-based model of Zijian Hong and Venkatasubramanian Viswanathan (https://doi.org/10.1021/acsenergylett.8b01009) . Some simpler examples were developed before for a first approach with FEniCS: heat equation and combustion model.

This repository is a curated roadmap for learning the core mathematics used in computer science, machine learning, and data science without getting lost in unnecessary detours. It organizes topics like algebra, calculus, linear algebra, probability, and statistics into a pragmatic sequence that favors intuition and problem-solving over purely formal proofs. The materials emphasize short, high-leverage resources—video lectures, concise notes, and hands-on exercises—that help you build momentum quickly. It also suggests checkpoints and practice ideas so you can test comprehension and move forward with confidence. The structure is useful both for newcomers who want a starting plan and for practitioners filling specific gaps before tackling ML or deep learning. ...

This is a template Python code set which makes it easy to use JModelica to solve optimal control problem. The template includes a sample model definition file (opt_definition.mop) and a .bat file (run_me.bat) to start its calculation. After download the template, immediately you can run JModelica by only double-clicking run_me.bat file, and obtain the optimization result. Please go to the page given below for the information of how to start to use this and its details.

In knight tour (kt) problem every chess square is visited exactly once using knight moves. In this game some knight moves already made and we have an incomplete kt. The object is to complete the kt by guessing missing moves.

This algorithm written in Python solves at least a subset of the Hamilton Circuit problem, which is NP complete, in n^3 time.

This application is designed to solve problems of transshipment. This problem of the graph theory consists in finding a negative stream on the arcs which minimizes the total cost of the transport in a network R(V,E,b,c) where : - V = vertex - E

JSalesman is a very efficient Travelling Salesman problem solver that produces high-quality outputs. JSalesman uses hierarchial and multi-level approaches to solve the problem fast.

program which solves the poisson problem using multigrid and finite elements