286 research outputs found
The general traffic assignment problem: a proximal point method for equilibrium computation with applications to the demand adjustment problem
An adaptation of the proximal algorithm for the traffic assignment problem under a user equilibrium formulation for a general asymmetric traffic network is presented in
this paper. It follows the recently published results of Pennanen regarding convergence under non monotonicity. As it is well known the problem can be formulated as
a variational inequality and the algorithmic solutions developed up to date guarantee convergence only under too restrictive conditions which are difficult to appear in practice.
In this paper it is also discussed the possibility of including the algorithm on a demand adjustment problem formulated as a bilevel program with lower level traffic
equilibrium constraints expressed as a variational inequality.Peer ReviewedPostprint (published version
International Conference on Continuous Optimization (ICCOPT) 2019 Conference Book
The Sixth International Conference on Continuous Optimization took place on the campus of the Technical University of Berlin, August 3-8, 2019. The ICCOPT is a flagship conference of the Mathematical Optimization Society (MOS), organized every three years. ICCOPT 2019 was hosted by the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin. It included a Summer School and a Conference with a series of plenary and semi-plenary talks, organized and contributed sessions, and poster sessions.
This book comprises the full conference program. It contains, in particular, the scientific program in survey style as well as with all details, and information on the social program, the venue, special meetings, and more
Supervised Classification and Mathematical Optimization
Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data
Supervised classification and mathematical optimization
Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely
useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data.Ministerio de Ciencia e InnovaciónJunta de Andalucí
A general equilibrium model for multi-passenger ridesharing systems with stable matching
This paper proposes a general equilibrium model for multi-passenger
ridesharing systems, in which interactions between ridesharing drivers,
passengers, platforms, and transportation networks are endogenously captured.
Stable matching is modeled as an equilibrium problem in which no ridesharing
driver or passenger can reduce ridesharing disutility by unilaterally switching
to another matching sequence. This paper is one of the first studies that
explicitly integrates the ridesharing platform multi-passenger matching problem
into the model. By integrating matching sequence with hyper-network,
ridesharing-passenger transfers are avoided in a multi-passenger ridesharing
system. Moreover, the matching stability between the ridesharing drivers and
passengers is extended to address the multi-OD multi-passenger case in terms of
matching sequence. The paper provides a proof for the existence of the proposed
general equilibrium. A sequence-bush algorithm is developed for solving the
multi-passenger ridesharing equilibrium problem. This algorithm is capable to
handle complex ridesharing constraints implicitly. Results illustrate that the
proposed sequence-bush algorithm outperforms general-purpose solver, and
provides insights into the equilibrium of the joint stable matching and route
choice problem. Numerical experiments indicate that ridesharing trips are
typically longer than average trip lengths. Sensitivity analysis suggests that
a properly designed ridesharing unit price is necessary to achieve network
benefits, and travelers with relatively lower values of time are more likely to
participate in ridesharing
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