What is she best known for?
The fields of study she is best known for:
- Mathematical optimization
- Computer network
- Algorithm
Martine Labbé mainly investigates Mathematical optimization, Facility location problem, Combinatorics, Time complexity and Integer programming.
Her Mathematical optimization research is multidisciplinary, incorporating elements of Routing, Computation and Graph.
Her research investigates the link between Graph and topics such as Exact algorithm that cross with problems in Discrete mathematics, Linear programming and Vertex.
In the field of Facility location problem, her study on 1-center problem overlaps with subjects such as Context, Function and Network analysis.
Her Combinatorics study integrates concerns from other disciplines, such as Flow, Upper and lower bounds and Projection.
Her research integrates issues of Extreme point, Transportation theory and Public transport in her study of Integer programming.
Her most cited work include:
- A bilevel model of taxation and its application to optimal highway pricing (265 citations)
- Location on networks (183 citations)
- A Bilevel Model for Toll Optimization on a Multicommodity Transportation Network (140 citations)
What are the main themes of her work throughout her whole career to date?
Martine Labbé spends much of her time researching Mathematical optimization, Combinatorics, Integer programming, Algorithm and Bilevel optimization.
Her Mathematical optimization course of study focuses on Time complexity and Computational complexity theory.
Her work in Combinatorics addresses subjects such as Upper and lower bounds, which are connected to disciplines such as Heuristics.
The study incorporates disciplines such as Feature selection, Support vector machine and Limit in addition to Integer programming.
Martine Labbé has researched Bilevel optimization in several fields, including Linear programming and Relaxation.
Her Facility location problem study introduces a deeper knowledge of Operations research.
She most often published in these fields:
- Mathematical optimization (51.75%)
- Combinatorics (17.12%)
- Integer programming (18.29%)
What were the highlights of her more recent work (between 2017-2021)?
- Mathematical optimization (51.75%)
- Bilevel optimization (14.01%)
- Integer programming (18.29%)
In recent papers she was focusing on the following fields of study:
The scientist’s investigation covers issues in Mathematical optimization, Bilevel optimization, Integer programming, Stackelberg competition and Time complexity.
Her Mathematical optimization research includes elements of Budget constraint and Limit.
Her research in Bilevel optimization intersects with topics in Feasible region, Karush–Kuhn–Tucker conditions, Linear programming, Branch and cut and Relaxation.
Her Integer programming study combines topics in areas such as Construct, Hyperplane, Feature selection and Support vector machine.
Her studies deal with areas such as Game theory, Convex hull and Minimum spanning tree as well as Stackelberg competition.
Her study in Time complexity is interdisciplinary in nature, drawing from both Flow, Dimension, Maximization, Computational complexity theory and System of polynomial equations.
Between 2017 and 2021, her most popular works were:
- Morphology and genomic hallmarks of breast tumours developed by ATM deleterious variant carriers. (14 citations)
- There's No Free Lunch: On the Hardness of Choosing a Correct Big-M in Bilevel Optimization (12 citations)
- Mixed Integer Linear Programming for Feature Selection in Support Vector Machine (11 citations)
In her most recent research, the most cited papers focused on:
- Mathematical optimization
- Computer network
- Algorithm
Mathematical optimization, Bilevel optimization, Time complexity, Integer programming and Linear programming are her primary areas of study.
Her work carried out in the field of Mathematical optimization brings together such families of science as Budget constraint, Support vector machine, Construct, Hyperplane and Limit.
The Bilevel optimization study combines topics in areas such as Feasible region, Benders' decomposition, Proxy, Heuristics and Combinatorial optimization.
Her Time complexity research integrates issues from Financial engineering, Computational complexity theory, Dynamic programming, Core and Network element.
Martine Labbé combines subjects such as Stackelberg competition, Simulation, Convex hull and Game theory with her study of Integer programming.
Her study looks at the intersection of Linear programming and topics like Discrete optimization with Branch and price, Exponential number and Applied mathematics.
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