183 skills found · Page 5 of 7
lazyprogrammer / Min Cost Network FlowSolve the min cost flow problem using both NetworkX and Linear Programming package PuLP
afish / MilpManagerLibrary for Mixed Integer Linear Programming. It contains abstraction for pluggable solvers and implementation of common mathematical functions.
mohsenpakzad / RustplexA linear programming solver based on the Simplex algorithm for Rust🦀
georgiakarataraki / Microgrid Optimization Model With Hydrogen StorageDeveloped and implemented a Mixed-Integer Linear Programming (MILP) model for microgrid operation optimization, leveraging renewable energy sources and hydrogen storage. The model simulates two operational modes—islanded and grid-connected—to achieve cost-efficient, sustainable energy management. Using Python and solved via the Gurobi optimizer.
leoschleier / AcopfImplementation of a successive linear programming algorithm (SLP) to solve the alternating current optimal power flow (ACOPF) problem.
chutrunganh / Simplex Method📌Simple Method in optimization problem, step by step with C language implementation.
yuhaozhang94 / Changi Airport Taxiway PlanningWe want to solve the coupled routing and timing optimization problem in taxiway planning. There are many individual constraints such as push-back times, taxiway layouts, and separation rules all requiring considerations. The dynamic nature of this problem requires planning to be updated regularly. We propose to formulate the optimization problem in a Receding Horizon scheme, meaning that a plan is implemented in small execution horizons, before re-planning occurs. We will have Changi Airport surface modelled as a graph containing nodes, each representing a junction of taxiways. We will construct the connectivity and distance matrix between nodes. Active aircrafts are then modelled as points moving along the arcs, subject to speed limit. Each aircraft moves from its origin node to its destination node, while observing taxiway rules. The decision variables are therefore 1) the node at which an aircraft begins its kth move 2) the time at which an aircraft starts its kth move. The objective is to minimize a weighted combination of total taxi time of all aircrafts and their total taxi distance. We will use Mixed Integer Linear Programming and CPLEX to solve the optimization problem. As taxiway planning is coupled with runway scheduling and other operations, we made assumptions to simplify the problem in order to define a realistic scope given the time constraint.
Aryia-Behroziuan / Robot LearningIn developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68]
igobrilhante / SimplexSimplex Method for solving linear programs
pakwah / Revised Simplex MethodA C++ implementation of the revised simplex method for solving linear programming problems
coin-or / CHiPPS BLISThis is the BiCePS Linear Integer Solver (BLIS), a parallel solver for mixed integer linear programs that is implemented on top of the BiCePS layer of the CHiPPS framework.
victorliu / Lp TinyA tiny linear program solver library.
tpawelski / Graph Coloring LpInteger Linear Programming apraoch to solving applications of the graph coloring problem
cran / LpSolve:exclamation: This is a read-only mirror of the CRAN R package repository. lpSolve — Interface to 'Lp_solve' v. 5.5 to Solve Linear/Integer Programs. Homepage: https://github.com/gaborcsardi/lpSolve Report bugs for this package: https://github.com/gaborcsardi/lpSolve/issues
harleym / JuLinear.jlThis repository implements a linear programming solver in julia.
danielptv / Simplex CalculatorJava tool for linear programming problems solving.
gtfactslab / LinraxJAX-compatible, simplex method-based linear program solver
OpenRulesSupport / Jsr331JCP Standard JSR331 “Java Constraint Programming API”. It is used for Modeling and Solving Constraint Satisfaction and Optimization Problems using Java and off-the-shelf Constraint/Linear Solvers
dungnv0696 / GomoryCutI implement two phase simplex method, dual simplex method, gomory-cut constraint to solve Integer Linear Programming
aig-upf / SoplexSoPlex is an optimization package for solving linear programming problems (LPs) based on an advanced implementation of the primal and dual revised simplex algorithm. It provides special support for the exact solution of LPs with rational input data. It can be used as a standalone solver reading MPS or LP format files via a command line interface as well as embedded into other programs via a C++ class library.
