326,334 research outputs found
Population-Based Reinforcement Learning for Combinatorial Optimization
Applying reinforcement learning (RL) to combinatorial optimization problems
is attractive as it removes the need for expert knowledge or pre-solved
instances. However, it is unrealistic to expect an agent to solve these (often
NP-)hard problems in a single shot at inference due to their inherent
complexity. Thus, leading approaches often implement additional search
strategies, from stochastic sampling and beam-search to explicit fine-tuning.
In this paper, we argue for the benefits of learning a population of
complementary policies, which can be simultaneously rolled out at inference. To
this end, we introduce Poppy, a simple theoretically grounded training
procedure for populations. Instead of relying on a predefined or hand-crafted
notion of diversity, Poppy induces an unsupervised specialization targeted
solely at maximizing the performance of the population. We show that Poppy
produces a set of complementary policies, and obtains state-of-the-art RL
results on three popular NP-hard problems: the traveling salesman (TSP), the
capacitated vehicle routing (CVRP), and 0-1 knapsack (KP) problems. On TSP
specifically, Poppy outperforms the previous state-of-the-art, dividing the
optimality gap by 5 while reducing the inference time by more than an order of
magnitude
Systems approaches and algorithms for discovery of combinatorial therapies
Effective therapy of complex diseases requires control of highly non-linear
complex networks that remain incompletely characterized. In particular, drug
intervention can be seen as control of signaling in cellular networks.
Identification of control parameters presents an extreme challenge due to the
combinatorial explosion of control possibilities in combination therapy and to
the incomplete knowledge of the systems biology of cells. In this review paper
we describe the main current and proposed approaches to the design of
combinatorial therapies, including the empirical methods used now by clinicians
and alternative approaches suggested recently by several authors. New
approaches for designing combinations arising from systems biology are
described. We discuss in special detail the design of algorithms that identify
optimal control parameters in cellular networks based on a quantitative
characterization of control landscapes, maximizing utilization of incomplete
knowledge of the state and structure of intracellular networks. The use of new
technology for high-throughput measurements is key to these new approaches to
combination therapy and essential for the characterization of control
landscapes and implementation of the algorithms. Combinatorial optimization in
medical therapy is also compared with the combinatorial optimization of
engineering and materials science and similarities and differences are
delineated.Comment: 25 page
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
Tailored retrieval of health information from the web for facilitating communication and empowerment of elderly people
A patient, nowadays, acquires health information from the Web mainly through a “human-to-machine”
communication process with a generic search engine. This, in turn, affects, positively or negatively, his/her
empowerment level and the “human-to-human” communication process that occurs between a patient and a
healthcare professional such as a doctor. A generic communication process can be modelled by considering
its syntactic-technical, semantic-meaning, and pragmatic-effectiveness levels and an efficacious
communication occurs when all the communication levels are fully addressed. In the case of retrieval of health
information from the Web, although a generic search engine is able to work at the syntactic-technical level,
the semantic and pragmatic aspects are left to the user and this can be challenging, especially for elderly
people. This work presents a custom search engine, FACILE, that works at the three communication levels
and allows to overcome the challenges confronted during the search process. A patient can specify his/her
information requirements in a simple way and FACILE will retrieve the “right” amount of Web content in a
language that he/she can easily understand. This facilitates the comprehension of the found information and
positively affects the empowerment process and communication with healthcare professionals
Witnessing eigenstates for quantum simulation of Hamiltonian spectra
The efficient calculation of Hamiltonian spectra, a problem often intractable
on classical machines, can find application in many fields, from physics to
chemistry. Here, we introduce the concept of an "eigenstate witness" and
through it provide a new quantum approach which combines variational methods
and phase estimation to approximate eigenvalues for both ground and excited
states. This protocol is experimentally verified on a programmable silicon
quantum photonic chip, a mass-manufacturable platform, which embeds entangled
state generation, arbitrary controlled-unitary operations, and projective
measurements. Both ground and excited states are experimentally found with
fidelities >99%, and their eigenvalues are estimated with 32-bits of precision.
We also investigate and discuss the scalability of the approach and study its
performance through numerical simulations of more complex Hamiltonians. This
result shows promising progress towards quantum chemistry on quantum computers.Comment: 9 pages, 4 figures, plus Supplementary Material [New version with
minor typos corrected.
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