Optimization Solver is a professional software for solving optimization problems. It can be used in a wide range of fields such as electrical energy, industrial manufacturing, transportation and logistics, retail, finance, and cloud computing. It is the core of industrial design software and is a powerful tool to help enterprises reduce costs and improve efficiency.
Overview
Optimization Solver is a professional software developed by the MindOpt Solver team from Decision Intelligence Lab of Alibaba DAMO Academy and can be used to solve optimization problems. It can be used in a wide range of fields such as cloud computing, electrical energy, industrial manufacturing, transportation and logistics, retail, and finance. Optimization Solver is the core of industrial design software and is a powerful tool in intelligent decision-making scenarios to help enterprises reduce costs and increase efficiency. Optimization Solver reduces hundreds of millions of Chinese yuan for Alibaba Cloud in elastic computing resource scheduling and optimization scenarios every year. The software can help design or optimize production solutions, appropriately allocate resources, and improve decision-making in various scenarios.
The following figure shows the problems that Optimization Solver can solve, technical advantages of Optimization Solver, and business benefits of Optimization Solver.
* This figure obtained from the MindOpt Solver team can help you understand the features of Optimization Solver.
Features
The Optimization Solver solution supports mathematical programming solving, simulation optimization, and online optimization. The three features can be used to solve problems whose quantifiable difficulties are different.
1. Mathematical programming solving
The mathematical programming solving feature can be used to solve mathematical programming problems. The problems are defined by a quantifiable formula in which objective functions, variables, and constraints are included. Mathematical programming includes linear programming (LP), nonlinear programming (NLP), and mixed integer programming (MIP).
The following solving capabilities are supported: LP, convex quadratic programming (QP), semidefinite programming (SDP), and mixed-integer linear programming (MILP). More software features are under development. We welcome you to stay tuned for our software update notifications.
2. Simulation optimization
Simulation optimization includes black-box optimization and zeroth-order (ZO) optimization. The simulation optimization feature can be used to solve complex optimization problems, optimization problems that cannot be parsed by using objective functions, or optimization problems whose constraints and other conditions cannot be quantifiable. For example, this feature can be used to solve problems of a simulation system. Optimization Solver obtains evaluation results of control parameters from the simulation system to infer and search for optimization solutions. The control parameters are input variables. The simulation optimization feature can be used for policy searches in reinforcement learning, industrial smelting solution design, and budget quota optimization of computing resources.
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3. Online optimization
The online optimization feature is applicable for real-world systems that contain unknown information. You can use this feature to optimize systems when they are running. The online optimization feature can be used in the following scenarios of online commodity systems: material selection, new product recommendation, throttling, and online intelligent distribution of rights and interests. The online commodity systems include e-commerce websites, video websites, and advertising websites.
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