scholarly journals Online Linear Optimization with Inventory Management Constraints

2020 ◽  
Vol 4 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Lin Yang ◽  
Mohammad H. Hajiesmaili ◽  
Ramesh Sitaraman ◽  
Adam Wierman ◽  
Enrique Mallada ◽  
...  
Keyword(s):  
Inventory Management ◽  
Linear Optimization ◽  
Online Linear Optimization

scholarly journals Online Linear Optimization with Inventory Management Constraints

2020 ◽  
Vol 48 (1) ◽  
pp. 7-8
Author(s):  
Lin Yang ◽  
Mohammad H. Hajiesmaili ◽  
Ramesh Sitaraman ◽  
Adam Wierman ◽  
Enrique Mallada ◽  
...  
Keyword(s):  
Inventory Management ◽  
Linear Optimization ◽  
Online Linear Optimization

scholarly journals Efficient Online Linear Optimization with Approximation Algorithms

2020 ◽  
Author(s):  
Dan Garber
Keyword(s):  
Approximation Algorithms ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Natural Extension ◽  
First Results ◽  
Linear Optimization Problems ◽  
Oracle Complexity ◽  
Approximation Guarantee ◽  
Factor Α ◽  
Online Linear Optimization

We revisit the problem of online linear optimization in the case where the set of feasible actions is accessible through an approximated linear optimization oracle with a factor α multiplicative approximation guarantee. This setting in particular is interesting because it captures natural online extensions of well-studied offline linear optimization problems that are NP-hard yet admit efficient approximation algorithms. The goal here is to minimize the α-regret, which is the natural extension to this setting of the standard regret in online learning. We present new algorithms with significantly improved oracle complexity for both the full-information and bandit variants of the problem. Mainly, for both variants, we present α-regret bounds of [Formula: see text], were T is the number of prediction rounds, using only [Formula: see text] calls to the approximation oracle per iteration, on average. These are the first results to obtain both the average oracle complexity of [Formula: see text] (or even polylogarithmic in T) and α -regret bound [Formula: see text] for a constant c > 0 for both variants.

scholarly journals Online Linear Optimization over Permutations

2011 ◽  
pp. 534-543 ◽  
Author(s):  
Shota Yasutake ◽  
Kohei Hatano ◽  
Shuji Kijima ◽  
Eiji Takimoto ◽  
Masayuki Takeda
Keyword(s):  
Linear Optimization ◽  
Online Linear Optimization

scholarly journals Online Linear Optimization with the Log-Determinant Regularizer

2018 ◽  
Vol E101.D (6) ◽  
pp. 1511-1520
Author(s):  
Ken-ichiro MORIDOMI ◽  
Kohei HATANO ◽  
Eiji TAKIMOTO
Keyword(s):  
Linear Optimization ◽  
Online Linear Optimization

Optimal Test Assembly in Practice

2013 ◽  
Vol 221 (3) ◽  
pp. 190-200 ◽  
Author(s):  
Jörg-Tobias Kuhn ◽  
Thomas Kiefer
Keyword(s):  
Particle Physics ◽  
Large Scale ◽  
Block Design ◽  
Linear Optimization ◽  
Mixed Integer ◽  
Optimal Test ◽  
Automated Test Assembly ◽  
Test Assembly ◽  
Block Level ◽  
Mixed Integer Linear Optimization

Several techniques have been developed in recent years to generate optimal large-scale assessments (LSAs) of student achievement. These techniques often represent a blend of procedures from such diverse fields as experimental design, combinatorial optimization, particle physics, or neural networks. However, despite the theoretical advances in the field, there still exists a surprising scarcity of well-documented test designs in which all factors that have guided design decisions are explicitly and clearly communicated. This paper therefore has two goals. First, a brief summary of relevant key terms, as well as experimental designs and automated test assembly routines in LSA, is given. Second, conceptual and methodological steps in designing the assessment of the Austrian educational standards in mathematics are described in detail. The test design was generated using a two-step procedure, starting at the item block level and continuing at the item level. Initially, a partially balanced incomplete item block design was generated using simulated annealing, whereas in a second step, items were assigned to the item blocks using mixed-integer linear optimization in combination with a shadow-test approach.

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