Number Of Restarts Research Articles

Comparing Male and Female Resident Physicians in Central Venous Catheter Insertion Self-confidence and Competency: A Retrospective Cohort Study.

Female physicians often report lower self-confidence in their procedural and clinical competency compared to male physicians. There is limited data regarding self-reported confidence of female versus male trainees and any relation to objective competency in central venous catheter insertion. To analyze differences between male and female trainees in self-confidence and skill-based outcomes in placing central venous catheters. Using data from a central venous catheter simulation training program at a large tertiary medical center, we performed linear regressions to analyze confidence difference pre- and post-training, number of restarts, and number of cannulation attempts while controlling for baseline demographic characteristics of the sample. PGY-1 physician residents in all residency specialties who insert central venous catheters in the clinical setting at a tertiary academic center with a sample size of 281 residents. Confidence difference pre- and post-training measured on a Likert scale 1-5, number of restarts (novel global assessment variable), and number of cannulation attempts during the competency evaluation. Female trainees had both lower pre-program confidence (1.35 versus 1.74 out of 5, p < 0.001) and lower post-program confidence (3.77 versus 4.12 out of 5, p = 0.0021) as compared to male trainees. There was no statistically significant difference in number of restarts (95% CI - 0.073 to 0.368, p = 0.185) or cannulation attempts (95% CI - 0.039 to 0.342, p = 0.117) between sexes in linear regressions controlled for age, specialty designation, prior central venous catheter training, prior ultrasound guided vessel cannulation training, and pre-training confidence level. Female trainees rated their confidence significantly lower than their male counterparts both before and after the training program, despite no significant difference in skill-based outcomes. We discuss potential implications for trainees acquiring procedural skills during residency and for physician educators as they design training programs and delegate procedural opportunities.

Multi‐start iterated local search metaheuristic for the multi‐mode resource‐constrained project scheduling problem

AbstractIn this paper a multi‐start iterated local search (MS‐ILS) algorithm is presented as a new and effective approach to solve the multi‐mode resource‐constrained project scheduling problem (MRCPSP). The MRCPSP is a well‐known project scheduling NP‐Hard optimization problem, in which there is a trade‐off between the duration of each project activity and the amount of resources they require to be completed. The proposed algorithm generates an initial solution, performs a local search to obtain a local optimum, subsequently, for a certain number of iterations, makes a perturbation to that local optimum and performs a new local search on the perturbed solution. This whole process then restarts with a different initial solution for a certain number of restarts. The algorithm was tested on benchmark instances of projects with 30, 50 and 100 activities from well‐known libraries. The obtained results were compared to recent benchmark results from the literature. The proposed algorithm outperforms other solution methods found in related literature for the largest tested instances (100 activities), while for smaller instances it shows to be quite competitive, in terms of the average deviation against known lower bounds.

Exploration of Hard to Solve 3-sat Problems

We designed and implemented an efficient tough random symmetric 3-SAT generator and propose two deterministic algorithms that efficiently generate 3-SAT instances with a unique solution. We quantify the first algorithms hardness in terms of CPU time, numbers of restarts, decisions, propagations, conflicts and conflicted literals that occur when a solver tries to solve 3-SAT instances. In this experiment, the clause variable ratio was chosen to be around the conventional critical phase transition number 4.24. The experiment shows that instances generated by our generator are significantly harder than instances generated by the Tough K-SAT generator. The two deterministic algorithms generate 3-SAT instances with the number of clauses scaling as 4n, where n is the number of variables, and (n+6), respectively. By combining these two algorithms along with a simple padding algorithm, we prove a hybrid algorithm that can generate n-variable instances with the number of clauses that scale between (n+6) and 7n(n-1)(n-2). Overall, all proposed SAT generators seek to explore unique difficult to solve SAT problems.

Adaptive multi-population inflationary differential evolution

This paper proposes a multi-population adaptive version of inflationary differential evolution algorithm. Inflationary differential evolution algorithm (IDEA) combines basic differential evolution (DE) with some of the restart and local search mechanisms of Monotonic Basin Hopping (MBH). In the adaptive version presented in this paper, the DE parameters { CR} and F are automatically adapted together with the size of the local restart bubble and the number of local restarts of MBH. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The algorithm has been extensively tested over more than fifty test functions from the competitions of the Congress on Evolutionary Computation (CEC), CEC 2005, CEC 2011 and CEC 2014, and compared against all the algorithms participating in those competitions. For each test function, the paper reports best, worst, median, mean and standard deviation values of the best minimum found by the algorithm. Comparisons with other algorithms participating in the CEC competitions are presented in terms of relative ranking, Wilcoxon tests and success rates. For completeness, the paper presents also the single population adaptive IDEA, that can adapt only textit{CR} and F, and shows that this simpler version can outperform the multi-population one if the radius of the restart bubble and the number of restarts are properly chosen.

A new proposal to improve the early iterations in the interior point method

We propose a modification that allows reducing the number of restarts in the computation of the Controlled Cholesky Factorization preconditioner. We use this preconditioner in the solution of linear systems arising from primal-dual interior point method. The Controlled Cholesky Factorization preconditioner depends on the fill-in parameter and the correction parameter that controls diagonal fault. We use geometric and algebraic tools to modify these parameters. In particular, we determine an equation whose exact solution avoids the diagonal fault. Numerical experiments with large-scale problems show that these modifications reduce the number of restarts. These experiments indicate that the new approach is robust and competitive.

A simple and robust Monte Carlo hybrid local search algorithm for the facility location problem

ABSTRACTIn this article, a new Monte Carlo hybrid local search algorithm (Hyb-LS) is proposed for solving the uncapacitated facility location problem. Hyb-LS is based on repeated sampling using two local search strategies based on best improvement and randomized neighbourhood search. A major advantage of Hyb-LS for its practical use is that the number of restarts is its only parameter to tune. The algorithm is also simple to reimplement, scalable and robust to changes in coefficients within a problem instance. The stopping criterion for local search is learned automatically. Experimental results are presented for four representative and contrasting cost and distance models. The results obtained by Hyb-LS are compared to the optimal or near-optimal solutions found by a mixed integer linear programming (MILP) solver with a generous time limit. For three out of the four models, Hyb-LS obtains better solutions than the upper bound found by the MILP solver for at least one instance.

An efficient modification of the Hestenes-Stiefel nonlinear conjugate gradient method with restart property

The conjugate gradient (CG) method is one of the most popular methods to solve nonlinear unconstrained optimization problems. The Hestenes-Stiefel (HS) CG formula is considered one of the most efficient methods developed in this century. In addition, the HS coefficient is related to the conjugacy condition regardless of the line search method used. However, the HS parameter may not satisfy the global convergence properties of the CG method with the Wolfe-Powell line search if the descent condition is not satisfied. In this paper, we use the original HS CG formula with a mild condition to construct a CG method with restart using the negative gradient. The convergence and descent properties with the strong Wolfe-Powell (SWP) and weak Wolfe-Powell (WWP) line searches are established. Using this condition, we guarantee that the HS formula is non-negative, its value is restricted, and the number of restarts is not too high. Numerical computations with the SWP line search and some standard optimization problems demonstrate the robustness and efficiency of the new version of the CG parameter in comparison with the latest and classical CG formulas. An example is used to describe the benefit of using different initial points to obtain different solutions for multimodal optimization functions.

On-line Data Retrieval Algorithm with Restart Strategy in Wireless Networks

Data retrieval is an efficient scheme for data dissemination in mobile computing, and its primary goal is to improve the waiting time and save the energy of mobile devices. Existing data retrieval algorithms are developed at the basis of a-priori knowledge of wireless data broadcast. In practical, they are not suitable for on-line data retrieval where the knowledge is not known. In this paper, we study on-line data retrieval problem with Las Vegas strategy of randomized algorithm, called as LVDR, and reduce the competitive rate of this problem, , where is the number of channels and is the length of broadcast cycle. Thus, we observe that different schemes of choosing channel will generate the different efficiencies in randomized algorithm. Therefore, we propose another scheme introduced restart strategy to execute the other retrieval sequences of choosing channels when the client may not retrieve the requested data item in certain time, called as LVDR-RS. In addition, we adopt Markov chain to predict the optimal restart cycle for each requested data item. Through analysis, we obverse that the competitive rate is closely related with the number of restarts and restart cycle

A Polynomial Preconditioned Global CMRH Method for Linear Systems with Multiple Right-Hand Sides

The restarted global CMRH method (Gl-CMRH(m)) (Heyouni, 2001) is an attractive method for linear systems with multiple right-hand sides. However, Gl-CMRH(m) may converge slowly or even stagnate due to a limited Krylov subspace. To ameliorate this drawback, a polynomial preconditioned variant of Gl-CMRH(m) is presented. We give a theoretical result for the square case that assures that the number of restarts can be reduced with increasing values of the polynomial degree. Numerical experiments from real applications are used to validate the effectiveness of the proposed method.

A local-restart coupled strategy for simultaneous sizing and geometry truss optimization

This paper presents an approach for the global optimization of truss sizing and geometry that is based on a probabilistic restart procedure coupled with a local search algorithm. The resulting algorithm is able to guarantee local optimality and provides a set of local optima which contain, with an increasing probability as the number of restarts increases, the global solution. The optimization problem searches for a truss structure of minimum volume, subject to stress constraints. The design variables are the bars cross-section areas and some nodal coordinates. Several loading conditions are also considered. Finally, four numerical examples are presented and the main aspects of the approach are discussed.

Recording and Minimizing Nogoods from Restarts

In this paper, nogood recording is investigated for CSP within the randomization and restart framework. Our goal is to avoid the same situations to occur from one run to the next ones. More precisely, nogoods are recorded when the current cutoff value is reached, i.e. before restarting the search algorithm. Such a set of nogoods is extracted from the last branch of the current search tree and exploited using the structure of watched literals originally proposed for SAT. We prove that the worst-case time complexity of extracting such nogoods at the end of each run is only O(n^2 d) where n is the number of variables of the constraint network and d the size of the greatest domain, whereas for any node of the search tree, the worst-case time complexity of exploiting these nogoods to enforce Generalized Arc Consistency (GAC) is O(n|B|) where |B| denotes the number of recorded nogoods. As the number of nogoods recorded before each new run is bounded by the length of the last branch, the total number of recorded nogoods is polynomial in the number of restarts. Interestingly, we show that when the minimization of the nogoods is envisioned with respect to an inference operator φ, it is possible to directly identify some nogoods that cannot be minimized. For φ = AC (i.e. for MAC), the worst-case time complexity of extracting minimal nogoods is slightly increased to O(en2 d3 ) where e is the number of constraints of the network. Experimentation over a wide range of CSP instances using a generic state-of-the-art CSP solver demonstrates the effectiveness of this approach. Recording nogoods (and in particular, minimal nogoods) from restarts significantly improves the robustness of the solver.

Optimistic concurrency control protocol for real-time databases

In recent years, the use of optimistic schemes for concurrency control in real-time database systems (RTDBS) has received more and more attention. Different real-time optimistic protocols have been proposed. They incorporate different priority conflict resolution methods into the validation phase of a transaction to give preferences for the commitment of higher priority transactions. However, some of these protocols suffer from the problem of heavy restart overheads. Restarting near-to-complete transactions greatly increases the system workload and the deadline missing probability of the transactions (Haritsa et al., 1992; Huang, 1991). In order to reduce the number of restarts, dynamic adjustment of the serialization order of the conflicting transactions has been suggested using timestamp intervals to record the transactions' serialization constraints (Lee and Son, 1993a, 1993b; Lee, 1994). However, these kinds of protocols have the problem of heavy implementation overheads as the timestamp intervals have to be checked and updated in case of any conflict even though the transactions are still in their read phases (Lee, 1994). In this paper, we propose a new real-time optimistic protocol which employs the notion of dynamic adjustment of serialization order by backward-adjusting the non-serious conflicting transactions before the committing transactions so that many restarts can be eliminated. In the protocol, the overhead to support dynamic adjustment of serialization order is small. All checking is performed at the validation test of a transaction so as to provide more freedom for the adjustments. No conflict and serialization constraints have to be recorded during the read phase of a transaction. Performance studies of the proposed protocol by varying different parameters indicate that it outperforms other protocols.

A substructuring application of the virtual crack extension method

AbstractThe virtual crack extension method (VCE) is a very effective tool with finite elements for the accurate evaluation of stress intensity factors in two‐ or three‐dimensional structures. The method utilizes the concept of extending any node on the crack profile in any required direction and by any required amount. A new facility stores a relevant substructure of crack tip stiffnesses so that any number of restarts, each with a new set of extensions, can be performed. Different nodes around three‐dimensional crack profiles can be considered, either at vertex or midside node positions. The extension of the vertex nodes, in particular, also involves moving adjacent midside nodes along the profile, and different ways of effecting this, together with the calculation of the relevant area change, have been introduced and are described along with illustrative examples.