Coresets are e cient representations of data sets such that models trained on the coreset are provably competitive with models trained on the original data set. As such, they have been successfully used to scale up clustering models such as K-Means and Gaussian mixture models to massive data sets. However, until now, the algorithms and the corresponding theory were usually specific to each clus...

Domain adaptation (DA) aims to transfer knowledge from a label-rich and related domain (source domain) label-scare (target domain). Pseudo-labeling has recently been widely explored used in DA. However, this line of research is still confined the inaccuracy pseudo labels. In paper, we explore imbalance issue performance among classes in-depth observe that worse performances all are likely furth...

In this paper, teaching–learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic job-shop scheduling problem. There are two sub problems in FJSP. They are routing problem and sequencing problem. If both the sub problems are solved simultaneously, t...

This paper presents a multi-criteria vehicle routing problem with soft time windows (VRPSTW) to mini-mize fleet cost, routes cost, and violation of soft time windows penalty. In this case, the fleet is heterogene-ous. The VRPSTW consists of a number of constraints in which vehicles are allowed to serve customers out of the desirable time window by a penalty. It is assumed that this relaxation a...

In the last lecture, we studied the KNAPSACK problem. The KNAPSACK problem is an NP-hard problem but does admit a pseudo-polynomial time algorithm and can be solved efficiently if the input size is small. We used this pseudo-polynomial time algorithm to obtain an FPTAS for KNAPSACK. In this lecture, we study another class of problems, known as strongly NP-hard problems. Definition 1 (Strongly N...