58,564 research outputs found
A parallel solver for the design of oil filters
Nowadays, it is widely recognized that computer simulation plays a crucial role in designing oil filters used in the automotive industry. However, even a single direct simulation of the flow usually requires significant computational resources. Thus, it is obvious that solution of optimization problems is only feasible using parallel computers and algorithms.In this paper, we present a general master-slave parallel template, which was specially designed for the easy integration of direct parallel solvers into a parallel optimization tool. We show how an already existing direct solver for the 3D simulation of flow through the oil filter is integrated into our template to obtain a parallel optimization solver. Some capabilities and performance of this solver are demonstrated by solving geometry optimization problem of a filter element
An Expandable Machine Learning-Optimization Framework to Sequential Decision-Making
We present an integrated prediction-optimization (PredOpt) framework to
efficiently solve sequential decision-making problems by predicting the values
of binary decision variables in an optimal solution. We address the key issues
of sequential dependence, infeasibility, and generalization in machine learning
(ML) to make predictions for optimal solutions to combinatorial problems. The
sequential nature of the combinatorial optimization problems considered is
captured with recurrent neural networks and a sliding-attention window. We
integrate an attention-based encoder-decoder neural network architecture with
an infeasibility-elimination and generalization framework to learn high-quality
feasible solutions to time-dependent optimization problems. In this framework,
the required level of predictions is optimized to eliminate the infeasibility
of the ML predictions. These predictions are then fixed in mixed-integer
programming (MIP) problems to solve them quickly with the aid of a commercial
solver. We demonstrate our approach to tackling the two well-known dynamic
NP-Hard optimization problems: multi-item capacitated lot-sizing (MCLSP) and
multi-dimensional knapsack (MSMK). Our results show that models trained on
shorter and smaller-dimensional instances can be successfully used to predict
longer and larger-dimensional problems. The solution time can be reduced by
three orders of magnitude with an average optimality gap below 0.1%. We compare
PredOpt with various specially designed heuristics and show that our framework
outperforms them. PredOpt can be advantageous for solving dynamic MIP problems
that need to be solved instantly and repetitively
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
Recent works in learning-integrated optimization have shown promise in
settings where the optimization problem is only partially observed or where
general-purpose optimizers perform poorly without expert tuning. By learning an
optimizer to tackle these challenging problems with as the
objective, the optimization process can be substantially accelerated by
leveraging past experience. The optimizer can be trained with supervision from
known optimal solutions or implicitly by optimizing the compound function
. The implicit approach may not require optimal solutions as
labels and is capable of handling problem uncertainty; however, it is slow to
train and deploy due to frequent calls to optimizer during both
training and testing. The training is further challenged by sparse gradients of
, especially for combinatorial solvers. To address these
challenges, we propose using a smooth and learnable Landscape Surrogate as
a replacement for . This surrogate, learnable by neural
networks, can be computed faster than the solver , provides dense
and smooth gradients during training, can generalize to unseen optimization
problems, and is efficiently learned via alternating optimization. We test our
approach on both synthetic problems, including shortest path and
multidimensional knapsack, and real-world problems such as portfolio
optimization, achieving comparable or superior objective values compared to
state-of-the-art baselines while reducing the number of calls to .
Notably, our approach outperforms existing methods for computationally
expensive high-dimensional problems
A Parallel General Purpose Multi-Objective Optimization Framework, with Application to Beam Dynamics
Particle accelerators are invaluable tools for research in the basic and
applied sciences, in fields such as materials science, chemistry, the
biosciences, particle physics, nuclear physics and medicine. The design,
commissioning, and operation of accelerator facilities is a non-trivial task,
due to the large number of control parameters and the complex interplay of
several conflicting design goals. We propose to tackle this problem by means of
multi-objective optimization algorithms which also facilitate a parallel
deployment. In order to compute solutions in a meaningful time frame a fast and
scalable software framework is required. In this paper, we present the
implementation of such a general-purpose framework for simulation-based
multi-objective optimization methods that allows the automatic investigation of
optimal sets of machine parameters. The implementation is based on a
master/slave paradigm, employing several masters that govern a set of slaves
executing simulations and performing optimization tasks. Using evolutionary
algorithms as the optimizer and OPAL as the forward solver, validation
experiments and results of multi-objective optimization problems in the domain
of beam dynamics are presented. The high charge beam line at the Argonne
Wakefield Accelerator Facility was used as the beam dynamics model. The 3D beam
size, transverse momentum, and energy spread were optimized
Survey on Combinatorial Register Allocation and Instruction Scheduling
Register allocation (mapping variables to processor registers or memory) and
instruction scheduling (reordering instructions to increase instruction-level
parallelism) are essential tasks for generating efficient assembly code in a
compiler. In the last three decades, combinatorial optimization has emerged as
an alternative to traditional, heuristic algorithms for these two tasks.
Combinatorial optimization approaches can deliver optimal solutions according
to a model, can precisely capture trade-offs between conflicting decisions, and
are more flexible at the expense of increased compilation time.
This paper provides an exhaustive literature review and a classification of
combinatorial optimization approaches to register allocation and instruction
scheduling, with a focus on the techniques that are most applied in this
context: integer programming, constraint programming, partitioned Boolean
quadratic programming, and enumeration. Researchers in compilers and
combinatorial optimization can benefit from identifying developments, trends,
and challenges in the area; compiler practitioners may discern opportunities
and grasp the potential benefit of applying combinatorial optimization
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