5,588 research outputs found
Exploration of the scalability of LocFaults approach for error localization with While-loops programs
A model checker can produce a trace of counterexample, for an erroneous
program, which is often long and difficult to understand. In general, the part
about the loops is the largest among the instructions in this trace. This makes
the location of errors in loops critical, to analyze errors in the overall
program. In this paper, we explore the scala-bility capabilities of LocFaults,
our error localization approach exploiting paths of CFG(Control Flow Graph)
from a counterexample to calculate the MCDs (Minimal Correction Deviations),
and MCSs (Minimal Correction Subsets) from each found MCD. We present the times
of our approach on programs with While-loops unfolded b times, and a number of
deviated conditions ranging from 0 to n. Our preliminary results show that the
times of our approach, constraint-based and flow-driven, are better compared to
BugAssist which is based on SAT and transforms the entire program to a Boolean
formula, and further the information provided by LocFaults is more expressive
for the user
Robust Temporal Logic Model Predictive Control
Control synthesis from temporal logic specifications has gained popularity in
recent years. In this paper, we use a model predictive approach to control
discrete time linear systems with additive bounded disturbances subject to
constraints given as formulas of signal temporal logic (STL). We introduce a
(conservative) computationally efficient framework to synthesize control
strategies based on mixed integer programs. The designed controllers satisfy
the temporal logic requirements, are robust to all possible realizations of the
disturbances, and optimal with respect to a cost function. In case the temporal
logic constraint is infeasible, the controller satisfies a relaxed, minimally
violating constraint. An illustrative case study is included.Comment: This work has been accepted to appear in the proceedings of 53rd
Annual Allerton Conference on Communication, Control and Computing,
Urbana-Champaign, IL (2015
Diagnosing Infeasible Optimization Problems Using Large Language Models
Decision-making problems can be represented as mathematical optimization
models, finding wide applications in fields such as economics, engineering and
manufacturing, transportation, and health care. Optimization models are
mathematical abstractions of the problem of making the best decision while
satisfying a set of requirements or constraints. One of the primary barriers to
deploying these models in practice is the challenge of helping practitioners
understand and interpret such models, particularly when they are infeasible,
meaning no decision satisfies all the constraints. Existing methods for
diagnosing infeasible optimization models often rely on expert systems,
necessitating significant background knowledge in optimization. In this paper,
we introduce OptiChat, a first-of-its-kind natural language-based system
equipped with a chatbot GUI for engaging in interactive conversations about
infeasible optimization models. OptiChat can provide natural language
descriptions of the optimization model itself, identify potential sources of
infeasibility, and offer suggestions to make the model feasible. The
implementation of OptiChat is built on GPT-4, which interfaces with an
optimization solver to identify the minimal subset of constraints that render
the entire optimization problem infeasible, also known as the Irreducible
Infeasible Subset (IIS). We utilize few-shot learning, expert chain-of-thought,
key-retrieve, and sentiment prompts to enhance OptiChat's reliability. Our
experiments demonstrate that OptiChat assists both expert and non-expert users
in improving their understanding of the optimization models, enabling them to
quickly identify the sources of infeasibility
Analyzing Multi-Objective Linear and Mixed Integer Programs by Lagrange Multipliers
A new method for multi-objective optimization of linear and mixed programs based on Lagrange multiplier methods is developed. The method resembles, but is distinct from, objective function weighting and goal programming methods. A subgradient optimization algorithm for selecting the multipliers is presented and analyzed. The method is illustrated by its application to a model for determining the weekly re-distribution of railroad cars from excess supply areas to excess demand areas, and to a model for balancing cost minimization against order completion requirements for a dynamic lot size model
Wireless Backhaul Node Placement for Small Cell Networks
Small cells have been proposed as a vehicle for wireless networks to keep up
with surging demand. Small cells come with a significant challenge of providing
backhaul to transport data to(from) a gateway node in the core network. Fiber
based backhaul offers the high rates needed to meet this requirement, but is
costly and time-consuming to deploy, when not readily available. Wireless
backhaul is an attractive option for small cells as it provides a less
expensive and easy-to-deploy alternative to fiber. However, there are multitude
of bands and features (e.g. LOS/NLOS, spatial multiplexing etc.) associated
with wireless backhaul that need to be used intelligently for small cells.
Candidate bands include: sub-6 GHz band that is useful in non-line-of-sight
(NLOS) scenarios, microwave band (6-42 GHz) that is useful in point-to-point
line-of-sight (LOS) scenarios, and millimeter wave bands (e.g. 60, 70 and 80
GHz) that are recently being commercially used in LOS scenarios. In many
deployment topologies, it is advantageous to use aggregator nodes, located at
the roof tops of tall buildings near small cells. These nodes can provide high
data rate to multiple small cells in NLOS paths, sustain the same data rate to
gateway nodes using LOS paths and take advantage of all available bands. This
work performs the joint cost optimal aggregator node placement, power
allocation, channel scheduling and routing to optimize the wireless backhaul
network. We formulate mixed integer nonlinear programs (MINLP) to capture the
different interference and multiplexing patterns at sub-6 GHz and microwave
band. We solve the MINLP through linear relaxation and branch-and-bound
algorithm and apply our algorithm in an example wireless backhaul network of
downtown Manhattan.Comment: Invited paper at Conference on Information Science & Systems (CISS)
201
Empirical Bounds on Linear Regions of Deep Rectifier Networks
We can compare the expressiveness of neural networks that use rectified
linear units (ReLUs) by the number of linear regions, which reflect the number
of pieces of the piecewise linear functions modeled by such networks. However,
enumerating these regions is prohibitive and the known analytical bounds are
identical for networks with same dimensions. In this work, we approximate the
number of linear regions through empirical bounds based on features of the
trained network and probabilistic inference. Our first contribution is a method
to sample the activation patterns defined by ReLUs using universal hash
functions. This method is based on a Mixed-Integer Linear Programming (MILP)
formulation of the network and an algorithm for probabilistic lower bounds of
MILP solution sets that we call MIPBound, which is considerably faster than
exact counting and reaches values in similar orders of magnitude. Our second
contribution is a tighter activation-based bound for the maximum number of
linear regions, which is particularly stronger in networks with narrow layers.
Combined, these bounds yield a fast proxy for the number of linear regions of a
deep neural network.Comment: AAAI 202
Contingency-Constrained Unit Commitment with Post-Contingency Corrective Recourse
We consider the problem of minimizing costs in the generation unit commitment
problem, a cornerstone in electric power system operations, while enforcing an
N-k-e reliability criterion. This reliability criterion is a generalization of
the well-known - criterion, and dictates that at least
fraction of the total system demand must be met following the failures of
or fewer system components. We refer to this problem as the
Contingency-Constrained Unit Commitment problem, or CCUC. We present a
mixed-integer programming formulation of the CCUC that accounts for both
transmission and generation element failures. We propose novel cutting plane
algorithms that avoid the need to explicitly consider an exponential number of
contingencies. Computational studies are performed on several IEEE test systems
and a simplified model of the Western US interconnection network, which
demonstrate the effectiveness of our proposed methods relative to current
state-of-the-art
PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulations
In recent years, the power system research community has seen an explosion of
novel methods for formulating and solving power network optimization problems.
These emerging methods range from new power flow approximations, which go
beyond the traditional DC power flow by capturing reactive power, to convex
relaxations, which provide solution quality and runtime performance guarantees.
Unfortunately, the sophistication of these emerging methods often presents a
significant barrier to evaluating them on a wide variety of power system
optimization applications. To address this issue, this work proposes
PowerModels, an open-source platform for comparing power flow formulations.
From its inception, PowerModels was designed to streamline the process of
evaluating different power flow formulations on shared optimization problem
specifications. This work provides a brief introduction to the design of
PowerModels, validates its implementation, and demonstrates its effectiveness
with a proof-of-concept study analyzing five different formulations of the
Optimal Power Flow problem
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