340 research outputs found
Basic Factors Influencing the Sustainable Development Capability of Chinese Property and Casualty Insurance Enterprises
Under the influence of financial crisis, the performance of Chinese property insurance enterprises keeps falling. The enterprises face the challenge of sustainable development. Based on capability theory, this paper analyzes the connotation of sustainable development ability; determines three main factors (capability resources, capability level and capability environment) and their sub-factors, lays out the structural model of the sustainable development capability of property insurance enterprises. In this model, capability environment is the basis and capability resources and capability level are the pillars. Through learning, enterprises can effectively combine capability resources and capability level,thus forms core competency
Study on Evaluation System of Sustainable Development Capability of Chinese Property and Casualty Insurance Enterprises
It is important but difficult for evaluating sustainable development capability of Chinese property and casualty insurance enterprises. From the perspective of core capability and based on previous study about factors and capability structure model, the paper builds up the first grade indexes of the evaluation, which include financial capability, learning and innovation capability, environment capability, management capability and corporate governance capability. Furthermore, the present study proposes the second grade indexes of first grade indexes
Nested Elimination: A Simple Algorithm for Best-Item Identification from Choice-Based Feedback
We study the problem of best-item identification from choice-based feedback.
In this problem, a company sequentially and adaptively shows display sets to a
population of customers and collects their choices. The objective is to
identify the most preferred item with the least number of samples and at a high
confidence level. We propose an elimination-based algorithm, namely Nested
Elimination (NE), which is inspired by the nested structure implied by the
information-theoretic lower bound. NE is simple in structure, easy to
implement, and has a strong theoretical guarantee for sample complexity.
Specifically, NE utilizes an innovative elimination criterion and circumvents
the need to solve any complex combinatorial optimization problem. We provide an
instance-specific and non-asymptotic bound on the expected sample complexity of
NE. We also show NE achieves high-order worst-case asymptotic optimality.
Finally, numerical experiments from both synthetic and real data corroborate
our theoretical findings.Comment: Accepted to ICML 202
Low Complexity SLP: An Inversion-Free, Parallelizable ADMM Approach
We propose a parallel constructive interference (CI)-based symbol-level
precoding (SLP) approach for massive connectivity in the downlink of multiuser
multiple-input single-output (MU-MISO) systems, with only local channel state
information (CSI) used at each processor unit and limited information exchange
between processor units. By reformulating the power minimization (PM) SLP
problem and exploiting the separability of the corresponding reformulation, the
original problem is decomposed into several parallel subproblems via the ADMM
framework with closed-form solutions, leading to a substantial reduction in
computational complexity. The sufficient condition for guaranteeing the
convergence of the proposed approach is derived, based on which an adaptive
parameter tuning strategy is proposed to accelerate the convergence rate. To
avoid the large-dimension matrix inverse operation, an efficient algorithm is
proposed by employing the standard proximal term and by leveraging the singular
value decomposition (SVD). Furthermore, a prox-linear proximal term is adopted
to fully eliminate the matrix inversion, and a parallel inverse-free SLP
(PIF-SLP) algorithm is finally obtained. Numerical results validate our
derivations above, and demonstrate that the proposed PIF-SLP algorithm can
significantly reduce the computational complexity compared to the
state-of-the-arts
ADMM-SLPNet: A Model-Driven Deep Learning Framework for Symbol-Level Precoding
Constructive interference (CI)-based symbol-level precoding (SLP) is an emerging downlink transmission technique for multi-antenna communications systems, and its low-complexity implementations are of practical importance. In this paper, we propose an interpretable model-driven deep learning framework to accelerate the processing of SLP. Specifically, the network topology is carefully designed by unrolling a parallelizable algorithm based on the proximal Jacobian alternating direction method of multipliers (PJ-ADMM), attaining parallel and distributed architecture. Moreover, the parameters of the iterative PJ-ADMM algorithm are untied to parameterize the network. By incorporating the problem-domain knowledge into the loss function, an unsupervised learning strategy is further proposed to discriminatively train the learnable parameters using unlabeled training data. Simulation results demonstrate significant efficiency improvement of the proposed ADMM-SLPNet over benchmark schemes
Multi-Armed Bandits with Abstention
We introduce a novel extension of the canonical multi-armed bandit problem
that incorporates an additional strategic element: abstention. In this enhanced
framework, the agent is not only tasked with selecting an arm at each time
step, but also has the option to abstain from accepting the stochastic
instantaneous reward before observing it. When opting for abstention, the agent
either suffers a fixed regret or gains a guaranteed reward. Given this added
layer of complexity, we ask whether we can develop efficient algorithms that
are both asymptotically and minimax optimal. We answer this question
affirmatively by designing and analyzing algorithms whose regrets meet their
corresponding information-theoretic lower bounds. Our results offer valuable
quantitative insights into the benefits of the abstention option, laying the
groundwork for further exploration in other online decision-making problems
with such an option. Numerical results further corroborate our theoretical
findings.Comment: Preprin
Speeding-up Symbol-Level Precoding Using Separable and Dual Optimizations
Symbol-level precoding (SLP) manipulates the transmitted signals to
accurately exploit the multi-user interference (MUI) in the multi-user
downlink. This enables that all the resultant interference contributes to
correct detection, which is the so-called constructive interference (CI). Its
performance superiority comes at the cost of solving a nonlinear optimization
problem on a symbol-by-symbol basis, for which the resulting complexity becomes
prohibitive in realistic wireless communication systems. In this paper, we
investigate low-complexity SLP algorithms for both phase-shift keying (PSK) and
quadrature amplitude modulation (QAM). Specifically, we first prove that the
max-min SINR balancing (SB) SLP problem for PSK signaling is not separable,
which is contrary to the power minimization (PM) SLP problem, and accordingly,
existing decomposition methods are not applicable. Next, we establish an
explicit duality between the PM-SLP and SB-SLP problems for PSK modulation. The
proposed duality facilitates obtaining the solution to the SB-SLP given the
solution to the PM-SLP without the need for one-dimension search, and vice
versa. We then propose a closed-form power scaling algorithm to solve the
SB-SLP via PM-SLP to take advantage of the separability of the PM-SLP. As for
QAM modulation, we convert the PM-SLP problem into a separable equivalent
optimization problem, and decompose the new problem into several simple
parallel subproblems with closed-form solutions, leveraging the proximal
Jacobian alternating direction method of multipliers (PJ-ADMM). We further
prove that the proposed duality can be generalized to the multi-level
modulation case, based on which a power scaling parallel inverse-free algorithm
is also proposed to solve the SB-SLP for QAM signaling. Numerical results show
that the proposed algorithms offer optimal performance with lower complexity
than the state-of-the-art.Comment: 30 pages, 11 figure
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