A Convex LASSO Framework for Linear Timetabling
نویسندگان
چکیده
منابع مشابه
A discrete-event optimization framework for mixed-speed train timetabling problem
Railway scheduling is a complex task of rail operators that involves the generation of a conflict-free train timetable. This paper presents a discrete-event simulation-based optimization approach for solving the train timetabling problem to minimize total weighted unplanned stop time in a hybrid single and double track railway networks. The designed simulation model is used as a platform for ge...
متن کاملLinear Ranking for Linear Lasso Programs
The general setting of this work is the constraint-based synthesis of termination arguments. We consider a restricted class of programs called lasso programs. The termination argument for a lasso program is a pair of a ranking function and an invariant. We present the— to the best of our knowledge—first method to synthesize termination arguments for lasso programs that uses linear arithmetic. W...
متن کاملA Standard Framework for Timetabling Problems
When timetabling experts are faced with a new timetabling problem, they usually develop a very specialised and optimised solution for this new underlying problem. One disadvantage of this strategy is that even slight changes of the problem description often cause a complete redesign of data structures and algorithms. Furthermore, other timetabling problems cannot be fit to the data structures p...
متن کاملA Comparative Framework for Preconditioned Lasso Algorithms
Consider the SVD X = UDV >, where U is n× n, V is p× p and D is an n× p “diagonal” matrix with entries d1 < . . . < dn. Define two groups of left and right singular vectors associated with the q smallest and n − q largest singular values. Let the groups be defined by Uq, Un−q and Vq, Vn−q . Suppose HJ chooses as its row-basis the n−q largest right singular vectors, Vn−q . Then, from Table 1 of ...
متن کاملA Comparative Framework for Preconditioned Lasso Algorithms
The Lasso is a cornerstone of modern multivariate data analysis, yet its performance suffers in the common situation in which covariates are correlated. This limitation has led to a growing number of Preconditioned Lasso algorithms that pre-multiply X and y by matrices PX , Py prior to running the standard Lasso. A direct comparison of these and similar Lasso-style algorithms to the original La...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3019598