Industrial-scale linear assignment problems (LAPs) are frequently encountered in various industrial scenarios, e.g., asset allocation within the domain of credit management. However, optimization algorithms for such problems (e.g., PJ-ADMM) are highly sensitive to hyper-parameters. Existing solving systems rely on empirical parameter selection, which is challenging to achieve convergence and extremely time-consuming. Additionally, the resulting parameter rules are often inefficient. To alleviate this issue, we propose RL-ISLAP, an efficient and lightweight Reinforcement Learning framework for Industrial-Scale Linear Assignment Problems. We formulate the hyper-parameter selection for PJ-ADMM as a sequential decision problem and leverage reinforcement learning to enhance its convergence. Addressing the sparse reward challenge inherent in learning policies for such problems, we devise auxiliary rewards to provide dense signals for policy optimization, and present a rollback mechanism to prevent divergence in the solving process. Experiments on OR-Library benchmark demonstrate that our method is competitive to SOTA stand-alone solvers. Furthermore, the scale-independent design of observations enables us to transfer the acquired hyper-parameter policy to a scenario of LAPs in varying scales. On two real-world industrial-scale LAPs with up to 10 millions of decision variables, our proposed RL-ISLAP achieves solutions of comparable quality in 2/3 of the time when compared to the SOTA distributed solving system employing fine-tuned empirical parameter rules.

Linear programming (LP) is arguably the most common optimization problem encountered in practical settings. Important examples include machine learning systems optimization, resource allocation, and other decision-making scenarios. However, even with state-of-the-art (SOTA) solvers, it is extremely challenging to solve large-scale problems arising in industry settings, which could have up to billions of decision variables and require solutions within a time limit to meet business demands. This paper proposes PDAS, a Practical Distributed ADMM System to solve such problems with a variant of the Alternating Direction Method of Multipliers (ADMM) algorithm. PDAS offers user-friendly interfaces and provides near-linear speedup thanks to its high scalability and excellent performance. It also comes with a failover mechanism to ensure the stability of the iterative process. The convergence, feasibility, and optimality of PDAS have been verified on two real-world data-sets, resulting in a 10-4 average relative deviation from Gurobi. Although SOTA solvers do have advantages if only considering the solving time when tested on five small and medium-sized public data-sets, PDAS is more promising after including the modeling time. Moreover, when used to solve large-scale LP problems with up to 109 decision variables and 104 constraints in three real-world scenarios, PDAS achieves at least 2x speedups, well beyond the capabilities of SOTA.

Online allocation is a critical challenge in constrained recommendation systems, where the distribution of goods, ads, vouchers, and other content to users with limited resources needs to be managed effectively. While the existing literature has made significant progress in improving recommendation algorithms for various scenarios, less attention has been given to developing and deploying industry-scale online allocation system in an efficient manner. To address this issue, this paper introduces an integrated and efficient learning framework in constrained recommendation scenarios at Alipay. The framework has been tested through experiments, demonstrating its superiority over other state-of-the-art methods.

The orderly operation and development of any system are indivisible from decision optimization. Several issues in life are applicable to the thought of optimization problems to resolve. In this digital age, the size of information and data is obtaining larger and the potency of problem determination is changing into more demanding. Though there're many solvers for specific optimization problems, in the face of large-scale scenarios, there's no single platform that concurrently addresses usability, solvers' uniformity, and computing efficiency. In this demo, we present AntOpt, a decision optimization platform that integrates large-scale distributed computing engines, optimization algorithm solvers and productized services.