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What is the Optimization Solver?

The Optimization Solver stands as professional software tailored for resolving optimization conundrums across a myriad of industries. Its utility extends to diverse sectors including electrical energy, industrial manufacturing, transportation and logistics, retail, finance, and cloud computing. Serving as the cornerstone of industrial design software, it empowers enterprises with the capability to curtail expenses and enhance operational efficiency.

Overview:

The Optimization Solver epitomizes professional software engineered to tackle optimization challenges. Versatile in application, it finds utility in a broad spectrum of domains such as cloud computing, electrical energy, industrial manufacturing, transportation and logistics, retail, and finance. The Optimization Solver stands as the linchpin of industrial design software, furnishing enterprises with a potent tool for intelligent decision-making, enabling cost reduction and efficiency enhancement. Notably, the Optimization Solver yields significant cost savings for Alibaba Cloud annually through elastic computing resource scheduling and optimization scenarios. Its capabilities extend to aiding in the design or refinement of production solutions, resource allocation optimization, and enhancing decision-making processes across various scenarios.

Features:

The Optimization Solver solution encompasses mathematical programming solving, simulation optimization, and online optimization functionalities, tailored to address problems of varying complexity.

1. Mathematical programming solving: This feature facilitates the resolution of mathematical programming problems defined by quantifiable formulas comprising objective functions, variables, and constraints. Mathematical programming encompasses linear programming (LP), nonlinear programming (NLP), and mixed integer programming (MIP). Supported solving capabilities include LP, convex quadratic programming (QP), semidefinite programming (SDP), and mixed-integer linear programming (MILP). Ongoing development efforts aim to enrich the software's capabilities further, with updates awaiting keen anticipation.

2. Simulation optimization: Simulation optimization encompasses black-box optimization and zeroth-order (ZO) optimization, ideal for tackling complex optimization challenges beyond the purview of objective functions or quantifiable constraints. This feature shines in scenarios requiring solutions from simulation systems, leveraging control parameters to infer and explore optimization solutions. Applications range from policy searches in reinforcement learning to industrial smelting solution design and computing resource budget quota optimization.

3. Online optimization: Tailored for real-world systems harboring unknown variables, the online optimization feature excels in optimizing systems in real-time operational environments. Deployable in various online commodity system scenarios such as material selection, new product recommendation, throttling, and online intelligent distribution of rights and interests, it finds utility in diverse platforms including e-commerce, video, and advertising websites.