Search Results for "problem a"
Sort By:
Showing 49 open source projects for "problem a"
View related business solutions-
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
1
OR-Tools - Google Optimization Tools
Google's software suite for combinatorial optimization
...This suite contains a number of solvers, namely: a constraint programming solver; a linear programming solver; wrappers for commercial solvers (like Gurobi or CPLEX) and other open source solvers (SCIP, GLPK, etc.); among others. OR-Tools is written in C++, but comes with wrappers for Python, C# and Java. Once you model your problem in your preferred programming language, you can then use any of OR-Tools’ available solvers. OR-Tools is an award-winning project, having won three gold medals in the 2020 MiniZinc Challenge, the international constraint programming competition. -
2
MCM-ICM
Mathematical Contest resources
MCM-ICM is a curated archive of Outstanding Winner (“O-奖/特等奖”) solution papers from the Mathematical Contest in Modeling and the Interdisciplinary Contest in Modeling, spanning the early 2000s through recent years. The repository is organized by year, with per-year folders that collect the top-ranked reports and, in later years, additional materials such as problem statements or problem notes when available. It has evolved from a single-maintainer project into a collaborative effort, with different contributors stewarding successive year ranges so the collection stays current. The contents skew toward MATLAB and LaTeX, reflecting the tools most winning teams use to build models and typeset their reports; you’ll typically see complete write-ups alongside code that implements optimization, simulation, statistical analysis, or other modeling pipelines. -
3
JuMP
Modeling language for Mathematical Optimization
JuMP is a modeling language and collection of supporting packages for mathematical optimization in Julia. JuMP makes it easy to formulate and solve a range of problem classes, including linear programs, integer programs, conic programs, semidefinite programs, and constrained nonlinear programs. JuMP is used to solve large-scale inventory routing problems at Renault, schedule trains at Thales Inc., plan power grid expansion at PSR, and route school buses. -
4
Mathematics Dataset
This dataset code generates mathematical question and answer pairs
...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. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
5
Digraph3
A collection of python3 modules for Algorithmic Decision Theory
...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. -
6
Continuation Core and Toolboxes (COCO)
Toolboxes for parameter continuation and bifurcation analysis.
Development platform and toolboxes for parameter continuation, e.g., bifurcation analysis of dynamical systems and constrained design optimization. This material is based upon work partially supported by the National Science Foundation under Grant No. 1016467 and the Danish research council (FTP) under the project number 0602-00753B. Any opinions, findings, and conclusions or recommendations expressed on this site are those of the authors and do not necessarily reflect the views of the... -
7
HungarianAlgorithm
An Ada implementation of the Hungarian Algorithm to minimize cost
The Hungarian algorithm can be described as optimally solving a workers versus jobs assignment problem that minimizes total cost. This implementation assumes a square cost matrix, i.e. the number of jobs equals the number of workers to do them. -
8
Billiards Everything
The Great Periodic Path Hunt at gwtokarsky.github.io
Researchers, Mathematicians and Enthusiasts. Download "Billiards Everything" to discover new periodic billiard paths, an unresolved problem from 1775! Download "Billiards Covers" to see the most current list of periodic paths in triangles. Follow "The Great Periodic Path Hunt" at gwtokarsky.github.io for the latest news. Email tokarsky@ualberta.ca for feedback. -
9
Billiards Covers
This zip jar contains the current coverings of periodic paths.
...Download "Billiards Covers" to see the most current list of periodic paths in triangles. Download "Billiards Everything" to discover new periodic billiard paths, an unresolved problem from 1775! Follow "The Great Periodic Path Hunt" at gwtokarsky.github.io for the latest news. Email tokarsky@ualberta.ca for feedback. -
One Monitoring Tool for IT, OT and Cloud | Free TrialModern infrastructure spans data centers, cloud platforms and factory floors, and every blind spot between them is a risk. PRTG supports SNMP, WMI, SSH and other standard protocols to monitor IT, OT and hybrid environments through one customizable dashboard. Build the views your team needs, from network health to application performance, without switching tools. Try PRTG free for 30 days now.
-
10
polynomial-roots-calculation
Application for calculating the real roots of a polynomial
...But I have been able to make it work with a 40-degree polynomial, with two very proximal zeroes arround x=1000. It needs a precision of arround 60 digits. The problem comes with very-high degree polynomials. The higher is the degree, the faster the execution time increases. But for common examples, the application does the job well. You will find more about it at this web site: https://www.frojasg1.com:8443/downloads_web/web/html/raicesDePolinomios.html?origin=sourceforge -
11
EVAlgebra - Clifford algebra calculator
A simple Clifford algebra calculator
...EVAlgebra support space dimension from 1 to 4 with arbitrry signature. It is fully documented at evalgebra.org. You may write your own scripts to resolve specific problem. -
12
Python/FEniCS Examples
phase-field simulation and other examples with Python/FEniCS
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. -
13
Mathematics
Accumulation of mathematical knowledge, matrix numerical optimization
...The repository organizes material across pure and applied mathematics, including calculus, linear algebra, probability, statistics, and optimization. It also extends into computational and algorithmic applications of mathematics, making it a useful reference for both academic study and practical problem-solving. The goal is to provide learners, researchers, and developers with a consolidated source of foundational and advanced mathematical concepts. By gathering a diverse set of topics into one repository, it supports continuous learning and interdisciplinary exploration. Its open source nature allows for community contributions and adaptation to different educational or research needs. -
14
SparesPOP is a MATLAB implementation of a sparse semidefinite programming (SDP) relaxation method proposed for polynomial optimization problems (POPs). Please send a message to kojima-spop@is.titech.ac.jp if you have any question and/or request. We also release SparsePOPC++ and SparsePOPC++-windows. Both implementations are SparsePOP which does not use MATLAB, but only C++. In particular, SparsePOPC++-windows contains a binary file which works on Windows, and does not require to compile.
-
15
Awesome Math
This is the Curriculum for "How to Learn Mathematics Fast"
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. ... -
16
TSP Solver and Generator
Generate and solve Travelling Salesman Problem tasks
TSPSG is intended to generate and solve Travelling Salesman Problem (TSP) tasks. It uses Branch and Bound method for solving. An input is a number of cities and a matrix of city-to-city travel prices. The matrix can be populated with random values in a given range (useful for generating tasks). The result is an optimal route, its price, step-by-step matrices of solving and solving graph. The task can be saved in internal binary format and opened later. -
17
popt4jlib
Parallel Optimization Library for Java
popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known... -
18
SCalc
Calculates the transportation cost seasonality indexes
Calculates the transportation cost seasonality indexes on the basis of the measurement approach by G. L. Lo Magno, M. Ferrante and S. De Cantis. -
19
Maps coloring
Four color theorem application
Search for a pencil and paper proof of the four color problem. See http://4coloring.wordpress.com for info. Please let me know what you think about this application. Also visit the youtube channel: http://www.youtube.com/user/mariostefanutti/videos -
20
Soduku-Solver
Soduku Solver solves 6x6 soduku of easy and medium level.
Soduku solver V 1.0 is Open Source project which solves the 6x6 Soduku problem of easy and medium level. It is a Open Source project which is available in github repository and this project welcome each contributor which helps to make this project better. Keywords : soduku solver , solve the soduku , soduku 6*6 , soduku 6x6 , solve my soduku problem , amazing software , artificial intelligence , best software , top software -
21
Opt4J
Modular Java framework for meta-heuristic optimization
...It contains a set of (multi-objective) optimization algorithms such as evolutionary algorithms (including SPEA2 and NSGA2), differential evolution, particle swarm optimization, and simulated annealing. The benchmarks that are included comprise ZDT, DTLZ, WFG, and the knapsack problem. The goal of Opt4J is to simplify the evolutionary optimization of user-defined problems as well as the implementation of arbitrary meta-heuristic optimization algorithms. For this purpose, Opt4J relies on a module-based implementation and offers a graphical user interface for the configuration as well as a visualization of the optimization process. -
22
This java program will ultimately solve grid-type logic problems if given enough clues in a pseudo english language. Features a user friendly GUI letting the user know the progression of the solving process based on the clues entered.
-
23
PortOpt
A portfolio-optimizer using Markowitz(1952) mean-variance model
...In order to minimise the variance it internally uses QuadProg++, a library that implement the algorithm of Goldfarb and Idnani for the solution of a (convex) Quadratic Programming problem by means of an active-set dual method. This solution is very efficient as it allows to solve hundred of thousand of portfolio problems in seconds. PortOpt runs as a text/console tool so it can be easily used in your own scripts. -
24
GDLOG
Implementation of the GNFS for discrete logarithm problem in GF(p)
GDLOG is an implementation of the General Number Field Sieve algorithm for discrete logarithm problem in GF(p). -
25
xorlisp
Bit level lambda continuations and nothing else - Queue automata
Not working yet. To deal with the Halting Problem, computing and data are navigated using debugger ops: linearForward and treeForward, which navigate an astronomically large bit string where 1 is ( and 0 is ). All pairs are derived from (). For example, true is represented as ((()())()), and false is (()(()())). It appears related to the church encoding of lambda where T chooses first parameter and F chooses second, of a pair.
