Optimization Solvers Product List and Ranking from 5 Manufacturers, Suppliers and Companies

Last Updated： Aggregation Period:Sep 24, 2025～Oct 21, 2025
This ranking is based on the number of page views on our site.

Optimization Solvers Manufacturer, Suppliers and Company Rankings

Last Updated： Aggregation Period:Sep 24, 2025～Oct 21, 2025
This ranking is based on the number of page views on our site.

  1. People, Dreams & Technologies Group Co., Ltd. Tokyo//Construction Consultant
  2. ログ・オプト Chiba//Information and Communications
  3. アルテアエンジニアリング Tokyo//others
  4. 4 オクトーバー・スカイ Tokyo//Information and Communications
  5. 4 テガラ 本社 Shizuoka//Trading company/Wholesale
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Optimization Solvers Product ranking

Last Updated： Aggregation Period:Sep 24, 2025～Oct 21, 2025
This ranking is based on the number of page views on our site.

  1. Quantum Annealing Mathematical Optimization Solver [For Education, Research, and PoC] People, Dreams & Technologies Group Co., Ltd.
  2. Scheduling Optimization Solver OptSeq ログ・オプト
  3. High-speed mathematical optimization solver "Gurobi Optimizer" オクトーバー・スカイ
  4. Constraint Optimization Solver SCOP ログ・オプト
  5. 4 Model Predictive Control / Embedded System Optimization Solver 'FORCESPRO' テガラ 本社

Optimization Solvers Product List

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High-speed mathematical optimization solver "Gurobi Optimizer"

High-speed mathematical optimization solver "Gurobi Optimizer"

Infinite possibilities with optimized solutions! State-of-the-art high-speed mathematical optimization solver.

The "Gurobi Optimizer" is a mathematical optimization solver for linear programming (LP), quadratic programming (QP), quadratic constrained programming (QCP), and mixed-integer programming (MIP) [mixed-integer linear programming (MILP), mixed-integer quadratic programming (MIQP), mixed-integer quadratic constrained programming (MIQCP)]. This product is designed to leverage the latest hardware architectures and multi-core processor technologies thoroughly, using advanced optimization algorithms to quickly derive reliable solutions. 【Features】 ○ Continuously updates to the world's fastest speed in third-party benchmarks ○ Easily constructs complex models ○ Simple transition to complete applications ○ Faster and uses less memory ○ Rich interfaces For more details, please contact us or download the catalog.

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  • IoT

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Constraint Optimization Solver SCOP

Constraint Optimization Solver SCOP

Solving large-scale combinatorial optimization problems quickly.

SCOP (Solver for Constraint Programming) is a solver designed to quickly solve large-scale combinatorial optimization problems. By using solution principles specialized for combinatorial optimization problems, SCOP can efficiently explore good solutions even for large-scale problems that traditional mathematical optimization solvers cannot handle. Features: By defining variables in a different way than mathematical optimization solvers, it can significantly reduce the number of variables, enabling fast solutions. It allows for a more natural logical constraint description (easier for humans to understand) compared to mathematical optimization solvers. Based on metaheuristics, it possesses world-class search capabilities. Even for large-scale problems, it can solve them extremely efficiently within limited computation time. It provides data input through a simple modeling language and a Python language interface.

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Scheduling Optimization Solver OptSeq

Scheduling Optimization Solver OptSeq

Fast solving of large-scale scheduling optimization.

OptSeq is a general-purpose scheduling optimization solver specialized in scheduling optimization. It allows for the description of various practical constraints and employs algorithms specifically designed for scheduling optimization problems, enabling it to produce good solutions in a short time for large-scale scheduling optimization problems that cannot be solved by mathematical optimization solvers. In OptSeq, while considering renewable resources such as machines and people, non-renewable resources that are consumed such as money and materials, setup times, interruptions during work, resource occupancy and non-occupancy during interruptions, parallel tasks, selection of work modes, and arbitrary time constraints between tasks, it is a solver capable of addressing due date minimization scheduling and mixed scheduling of forward and backward scheduling. Features: It allows for modeling scheduling optimization problems in a more natural expression (easier for humans to understand) compared to mathematical optimization solvers. Based on metaheuristics, it possesses world-class search capabilities. Even for large-scale problems, it can solve them extremely efficiently within limited computation time. It provides data input through a simple modeling language and a Python language interface.

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Model Predictive Control / Embedded System Optimization Solver 'FORCESPRO'

Model Predictive Control / Embedded System Optimization Solver 'FORCESPRO'

A fast and reliable solver for model predictive control and embedded system optimization.

An advanced numerical optimization algorithm specially developed for the mathematical structure of optimal control problems solves complex optimization issues in milliseconds. This fastest solver also has a very small file size, allowing it to be embedded in any hardware platform, significantly improving safety, productivity, and energy efficiency. For example, it is used in autonomous driving (vehicle motion planning and energy management), rocket guidance and motion planning, pitch control of wind turbines, sheet metal laser cutting, and motion planning for robotic arms.

  • IT Control and Services

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Structural Analysis and Structural Optimization Solver Altair OptiStruct

Structural Analysis and Structural Optimization Solver Altair OptiStruct

It is the latest structural analysis solver for linear and nonlinear simulations under static and dynamic loads, capable of performance evaluation and optimization for strength, durability, NVH, and more.

<Main Features of the Product> • Supports nonlinear analysis • State-of-the-art NVH analysis solver • Highly parallelized solver • Structural optimization technology with 30 years of proven experience • Advanced optimization capabilities for laminated composites

  • Scientific Calculation and Simulation Software

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Quantum Annealing Mathematical Optimization Solver [For Education, Research, and PoC]

Quantum Annealing Mathematical Optimization Solver [For Education, Research, and PoC]

A mathematical optimization solver utilizing quantum annealing is a tool that efficiently solves complex optimization problems using quantum technology.

Pioneering the future with an innovative mathematical optimization solver! Our mathematical optimization solver is a next-generation solution that leverages quantum annealing technology. In today's society, faced with complex and vast combinatorial problems, it is capable of solving challenges that traditional algorithms struggle to address, quickly and efficiently. It is utilized across various industrial sectors, such as optimizing transportation networks, streamlining logistics routes, and optimizing portfolios, achieving significant cost reductions and productivity improvements. Particularly in scenarios where real-time decision-making is required, our solver demonstrates unparalleled performance. Moreover, as a method of mathematical optimization, quantum annealing excels in problems without data trends, allowing for solutions to be found in a very short time. A mathematical optimization solver that opens up future possibilities. Why not embark on a new challenge with us to pursue optimal solutions?

  • Scientific Calculation and Simulation Software

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Delivery Optimization Solver METRO

Delivery Optimization Solver METRO

Fast solving of large-scale distribution optimization problems.

It is a solver specialized in delivery optimization problems, capable of solving large-scale delivery optimization issues quickly. It can express almost all practical constraints of delivery optimization problems, making it a highly practical delivery optimization solver. • It is possible to optimize delivery from multiple sets of customer locations to customer locations (such as delivery for sharing services). • It can optimize delivery based on warehouses or distribution centers. • Practical constraints such as time constraints, vehicle and location-related constraints, driver breaks, multidimensional capacity of trucks (considering mixed loads of refrigerated and frozen items, etc.), and simultaneous pickups and deliveries can be described.

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Set Covering Optimization Solver OptCover

Set Covering Optimization Solver OptCover

Fast solving of large-scale set cover optimization problems.

OptCover is a solver designed to quickly solve large-scale set cover optimization problems. The set cover optimization problem (not a strict definition) involves enumerating feasible solutions and finding the best combination among them. It can solve any problem where feasible solutions can be enumerated, including delivery optimization problems, scheduling problems, and other combinatorial optimization problems. Features: • Based on metaheuristics, it has world-class search capabilities. It can efficiently solve large-scale problems within a limited computation time. • Data input is possible using a simple modeling language.

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Mathematical Optimization Solver Gurobi Optimizer

Mathematical Optimization Solver Gurobi Optimizer

A mathematical optimization solver boasting world-class performance.

It is one of the most widely used and high-performance mathematical optimization solvers in the world, with a broad range of applicable problem types. While the performance of the solver itself is important, the modeling often leads to significant differences in the scale of problems that can be solved and the speed of solutions. Our company not only sells the solver but also excels in the technical aspects of modeling, providing excellent modeling support (for a fee) to user companies, as well as various consulting services (for a fee) to solve real-world problems using mathematical optimization techniques. Types of problems that can be solved: - Linear Programming (LP) - Mixed Integer Linear Programming (MILP) - Quadratic Programming (QP) - Quadratic Constraints (QCP) - Mixed Integer Quadratic Programming (MIQP) - Mixed Integer Quadratic Constraints (MIQCP) - Mixed Integer Non-convex Quadratic Constraints (Non-convex MIQCP) Features: - Code designed to maximize parallel processing - Unmatched performance in cutting plane processing - Rapidly finds feasible solutions through advanced MIP heuristic algorithms - Barrier algorithms that fully leverage the latest computer architectures - Support for an intuitive, easy-to-use, and lightweight range of APIs

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