A General Large Neighborhood Search Framework for Solving Integer Programs

This paper studies how to design abstractions of large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways, and that are amenable to data-driven design. The goal is to arrive at new approaches that can reliably outperform existing solvers in wall-clock time. We focus on solving integer programs, and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed. The appeal of LNS is that it can easily use any existing solver as a subroutine, and thus can inherit the benefits of carefully engineered heuristic approaches and their software implementations. We also show that one can learn a good neighborhood selector from training data. Through an extensive empirical validation, we demonstrate that our LNS framework can significantly outperform, in wall-clock time, compared to state-of-the-art commercial solvers such as Gurobi

A General Large Neighborhood Search Framework for Solving Integer Programs

This paper studies how to design abstractions of large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways, and that are amenable to data-driven design. The goal is to arrive at new approaches that can reliably outperform existing solvers in wall-clock time. We focus on solving integer programs, and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed. The appeal of LNS is that it can easily use any existing solver as a subroutine, and thus can inherit the benefits of carefully engineered heuristic approaches and their software implementations. We also show that one can learn a good neighborhood selector from training data. Through an extensive empirical validation, we demonstrate that our LNS framework can significantly outperform, in wall-clock time, compared to state-of-the-art commercial solvers such as Gurobi

Antibacterial and Antiviral Properties of Coriandrum Sativum and Zingiber Officinale against Human Respiratory Tract Related Bacterial and Viral Infections: A Review with a Focus on the Case of SARS-CoV

Phytochemical constituents in extracts from medicinal plants have been widely used since ancient times to treat microbial infections. Coriandrum sativum and Zingiber officinale are two of the main popular ingredients in traditional medicine recipes. Currently, these extracts are used to prevent Covid-19 infections. Therefore, this review describes the antimicrobial properties of coriander and ginger and how far it is suitable to use against bacterial and viral infections occurring in the human respiratory tract. For instance, the main phytochemical available in C. sativum is linalool, followed by terpinene, pinene, cymene, decenal, and camphor. Gingerol is the main constituent in Z. officinale followed by shogaols and paradols. Moreover, many research findings revealed that the extract from coriander and ginger can be used to control respiratory tract infected pathogens due to the antiviral and antibacterial properties of available phytochemicals. Therefore, it is very effective to use coriander and ginger to boost the immune system. Furthermore, scientific evidence has proved the effective antiviral properties of compounds present in coriander and ginger that have binding affinity to the proteins in the virus, blocking the virus's receptors and boosting the immunity to face the COVID-19 situation. In fact, the effectiveness of the antimicrobial activity of mixed extract of medicinal plant parts is better than that of individuals. Therefore, this will review the therapeutic characteristics of coriander and ginger extracts due to their various phytochemical activities.

Learning to Search via Retrospective Imitation

We study the problem of learning a good search policy from demonstrations for combinatorial search spaces. We propose retrospective imitation learning, which, after initial training by an expert, improves itself by learning from its own retrospective solutions. That is, when the policy eventually reaches a feasible solution in a search tree after making mistakes and backtracks, it retrospectively constructs an improved search trace to the solution by removing backtracks, which is then used to further train the policy. A key feature of our approach is that it can iteratively scale up, or transfer, to larger problem sizes than the initial expert demonstrations, thus dramatically expanding its applicability beyond that of conventional imitation learning. We showcase the effectiveness of our approach on two tasks: synthetic maze solving, and integer program based risk-aware path planning

Learning to Search via Retrospective Imitation

We study the problem of learning a good search policy from demonstrations for combinatorial search spaces. We propose retrospective imitation learning, which, after initial training by an expert, improves itself by learning from its own retrospective solutions. That is, when the policy eventually reaches a feasible solution in a search tree after making mistakes and backtracks, it retrospectively constructs an improved search trace to the solution by removing backtracks, which is then used to further train the policy. A key feature of our approach is that it can iteratively scale up, or transfer, to larger problem sizes than the initial expert demonstrations, thus dramatically expanding its applicability beyond that of conventional imitation learning. We showcase the effectiveness of our approach on two tasks: synthetic maze solving, and integer program based risk-aware path planning