Optimal training of integer-valued neural networks with mixed integer programming [PDF]
PLoS ONE, 2023 Recent work has shown potential in using Mixed Integer Programming (MIP) solvers to optimize certain aspects of neural networks (NNs). However the intriguing approach of training NNs with MIP solvers is under-explored.
Tómas Thorbjarnarson, Neil Yorke-Smith
doaj +3 more sources
Adaptive Cut Selection in Mixed-Integer Linear Programming [PDF]
Open Journal of Mathematical Optimization, 2023 Cutting plane selection is a subroutine used in all modern mixed-integer linear programming solvers with the goal of selecting a subset of generated cuts that induce optimal solver performance.
Turner, Mark +3 more
doaj +2 more sources
Comparative Network Reconstruction using mixed integer programming. [PDF]
Bioinformatics, 2018 Abstract Motivation Signal-transduction networks are often aberrated in cancer cells, and new anti-cancer drugs that specifically target oncogenes involved in signaling show great clinical promise.
Bosdriesz E +7 more
europepmc +6 more sources
Mixed Integer Linear Programming Formulation Techniques [PDF]
SIAM Review, 2014 A wide range of problems can be modeled as Mixed Integer Linear Programming (MIP) problems using standard formulation techniques. However, in some cases the resulting MIP can be either too weak or too large to be effectively solved by state of the art ...
Vielma, Juan Pablo
core +5 more sources
Mixed-integer nonlinear programming 2018 [PDF]
Optimization and Engineering, 2019 Mixed-Integer Nonlinear Programming (MINLP) is the area of optimization that addresses nonlinear problems with continuous and integer variables. MINLP has proven to be a powerful tool for modeling. At the same time, it combines algorithmic design challenges from combinatorial and nonlinear optimization.
N. Sahinidis
semanticscholar +2 more sources
A multi-objective multi-period mathematical programming model for integrated project portfolio optimization and contractor selection [PDF]
MethodsXThis paper addresses the challenges of project portfolio optimization and contractor selection through two proposed scenarios. In the first scenario, two separate mixed-integer mathematical programming models are presented: one for project portfolio ...
Mostafa Zahedirad +3 more
doaj +2 more sources
Mixed-Integer Programming for Signal Temporal Logic With Fewer Binary Variables [PDF]
IEEE Control Systems Letters, 2022 Signal Temporal Logic (STL) provides a convenient way of encoding complex control objectives for robotic and cyber-physical systems. The state-of-the-art in trajectory synthesis for STL is based on Mixed-Integer Convex Programming (MICP).
Vince Kurtz, Hai Lin
semanticscholar +1 more source
A Survey for Solving Mixed Integer Programming via Machine Learning [PDF]
Neurocomputing, 2022 This paper surveys the trend of leveraging machine learning to solve mixed integer programming (MIP) problems. Theoretically, MIP is an NP-hard problem, and most of the combinatorial optimization (CO) problems can be formulated as the MIP.
Jiayi Zhang +5 more
semanticscholar +1 more source
Real-Time Mixed-Integer Quadratic Programming for Vehicle Decision-Making and Motion Planning [PDF]
IEEE Transactions on Control Systems Technology, 2023 We develop a real-time feasible mixed-integer programming-based decision-making (MIP-DM) system for automated driving (AD). Using a linear vehicle model in a road-aligned coordinate frame, the lane change constraints, collision avoidance, and traffic ...
R. Quirynen +2 more
semanticscholar +1 more source
A computational status update for exact rational mixed integer programming [PDF]
Mathematical programming, 2021 The last milestone achievement for the roundoff-error-free solution of general mixed integer programs over the rational numbers was a hybrid-precision branch-and-bound algorithm published by Cook, Koch, Steffy, and Wolter in 2013.
L. Eifler, Ambros M. Gleixner
semanticscholar +1 more source