Search · four archives
Search · four archives
14 papers · ranked by Valyu relevance
Jiayi Zhang, Chang Liu, Junchi Yan, Xijun Li + 2 more
'Mingxuan Yuan'] This paper surveys the trend of leveraging machine learning to solve mixed integer programming (MIP) problems. Theoretically, MIP is an NPhard problem, and most of the combinatorial optimization (CO) problems can be formulated as the MIP. Like other CO problems, the human-designed heuristic algorithms…
Luke Fina, Christopher Petersen, Matthew Hale
Feasibility Guarantees Authors: ['Luke Fina' 'Christopher Petersen' 'Matthew Hale'] In this paper we solve mixed-integer linear programs (MILPs) via distributed asynchronous saddle point computation. To solve a MILP, we relax it with a linear program approximation. We first show that if the linear program relaxation…
Weili Zhang, Charles Nicholson
The objective scaling ensemble approach is a novel two-phase heuristic for integer linear programming problems shown to be effective on a wide variety of integer linear programming problems. The technique identifies and aggregates multiple partial solutions to modify the problem formulation and significantly reduce the…
Authors not listed
Automated chemistry platforms hold the potential to enable large-scale organic synthesis campaigns, such as producing a library of compounds for biological evaluation. The efficiency of such platforms will depend on the schedule according to which the synthesis operations are executed. In this work, we study the…
Behrooz Bodaghi, Nadezda Sukhorukova
In this paper we propose a new efficient linear programming based approach for multi-resource allocation and location problems in disaster management. Such problems require an integer solution and therefore, in most cases, the computations rely on integer and mixed-integer linear programming solvers. In general, these…
Zayn Wang
Mixed-Integer Programming (MIP), particularly Mixed-Integer Linear Programming (MILP) and Mixed-Integer Quadratic Programming (MIQP), has found extensive applications in domains such as portfolio optimization and network flow control, which inclusion of integer variables or cardinality constraints renders these…
Gioni Mexi, Sébastien Designolle, Mathieu Besançon
We propose a primal heuristic for quadratic mixed-integer problems. Our method extends the Boscia framework – originally a mixedinteger convex solver leveraging a Frank-Wolfe-based branch-and-bound approach – to address nonconvex quadratic objective functions and constraints. We reformulate nonlinear constraints…
Authors not listed
Experimental design plays an important role in efficiently acquiring informative data for system characterization and deriving robust conclusions under resource limitations. Recent advancements in high-throughput experimentation coupled with machine learning have notably improved experimental procedures. While Bayesian…
Sleiman Mhanna, Isam Saedi, Pierluigi Mancarella
—In light of the increasing coupling between electricity and gas networks, this paper introduces two novel iterative methods for efficiently solving the multiperiod optimal electricity and gas flow (MOEGF) problem. The first is an iterative MILPbased method and the second is an iterative LP-based method with an…
Authors not listed
We present a vector-based method to balance chemical reactions. The algorithm builds candidates in a deterministic way, removes duplicates, and always prints coefficients in the lowest whole-number form. For redox cases, electrons and protons/hydroxide are treated explicitly, so both mass and charge are balanced. We…
Authors not listed
Solving optimization problems, especially for nonlinear and constrained systems, is a challenge. Decades of specialized algorithms have been developed for general and special cases of root finding, minimization (including constraints), for parameter estimation, and mapping connected spaces. These approaches typically…
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
Liwei Cao, Danilo Russo, Vassilios S. Vassiliadis, Alexei Lapkin
A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor…
Tobias Seidel, Lena-Marie Ränger, Thomas Grützner, Michael Bortz
In this work we present a new approach that we use to simulate and optimize multiple dividing wall columns at the same time. Instead of considering all model equations as constraints and all process variables as optimization variables in a large and highly nonlinear optimization problem we only incorporate a subset of…