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Can Linear Superiorization Be Useful for Linear Optimization Problems? [PDF]
Inverse Probl, 2017 Linear superiorization (LinSup) considers linear programming problems but instead of attempting to solve them with linear optimization methods it employs perturbation resilient feasibility-seeking algorithms and steers them toward reduced (not ...
Censor Y.
europepmc +3 more sources
The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization [PDF]
IEEE Transactions on Information Theory, 2017 We propose a novel high-dimensional linear regression estimator: the Discrete Dantzig Selector, which minimizes the number of nonzero regression coefficients subject to a budget on the maximal absolute correlation between the features and residuals ...
Mazumder, Rahul, Radchenko, Peter
core +2 more sources
Iterated linear optimization [PDF]
Quarterly of Applied Mathematics, 2021 We introduce a fixed point iteration process built on optimization of a linear function over a compact domain. We prove the process always converges to a fixed point and explore the set of fixed points in various convex sets. In particular, we consider elliptopes and derive an algebraic characterization of their fixed points.
Felzenszwalb, Pedro F. +2 more
openaire +3 more sources
Bilevel linear optimization belongs to NP and admits polynomial-size KKT-based reformulations [PDF]
Operations Research Letters, 2023 It is a well-known result that bilevel linear optimization is NP-hard. In many publications, reformulations as mixed-integer linear optimization problems are proposed, which suggests that the decision version of the problem belongs to NP. However, to the
C. Buchheim
semanticscholar +1 more source
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization [PDF]
Trans. Mach. Learn. Res., 2023 Learning Markov decision processes (MDP) in an adversarial environment has been a challenging problem. The problem becomes even more challenging with function approximation, since the underlying structure of the loss function and transition kernel are ...
Fang-yuan Kong +3 more
semanticscholar +1 more source
On the Complexity of Inverse Mixed Integer Linear Optimization [PDF]
SIAM Journal on Optimization, 2021 Inverse optimization is the problem of determining the values of missing input parameters for an associated forward problem that are closest to given estimates and that will make a given target vector optimal.
A. Bulut, T. Ralphs
semanticscholar +1 more source
Energies, 2023 To limit climate change, decarbonization of the transportation sector is necessary. The change from conventional combustion vehicles to vehicles with electric drives is already taking place.
Yannick Pohlmann, Carl-Friedrich Klinck
doaj +1 more source
A new algorithm for solving linear programming problems with bipolar fuzzy relation equation constraints [PDF]
Iranian Journal of Numerical Analysis and Optimization, 2021 This paper studies the linear optimization problem subject to a system of bipolar fuzzy relation equations with the max-product composition operator.
S. Aliannezhadi, A. Abbasi Molai
doaj +1 more source
Journal of Numerical Analysis and Approximation Theory, 2023 Our aim in this work is to extend the primal-dual interior point method based on a kernel function for linear fractional problem. We apply the techniques of kernel function-based interior point methods to solve a standard linear fractional program.
Mousaab Bouafia, Adnan Yassine
doaj +1 more source
A new search direction for full-Newton step infeasible interior-point method in linear optimization
Croatian Operational Research Review, 2023 In this work, we investigate a full Newton step infeasible interior-point method for linear optimization based on a new search direction which is obtained from an algebraic equivalent transformation of the central path system.
Behrouz Kheirfam
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