Julia L. Higle

2.1k total citations
33 papers, 1.2k citations indexed

About

Julia L. Higle is a scholar working on Management Science and Operations Research, Control and Systems Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Julia L. Higle has authored 33 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Management Science and Operations Research, 9 papers in Control and Systems Engineering and 7 papers in Computational Theory and Mathematics. Recurrent topics in Julia L. Higle's work include Risk and Portfolio Optimization (15 papers), Optimization and Mathematical Programming (9 papers) and Optimization and Variational Analysis (7 papers). Julia L. Higle is often cited by papers focused on Risk and Portfolio Optimization (15 papers), Optimization and Mathematical Programming (9 papers) and Optimization and Variational Analysis (7 papers). Julia L. Higle collaborates with scholars based in United States, Norway and China. Julia L. Higle's co-authors include Suvrajeet Sen, Robert L. Smith, James C. Bean, Stein W. Wallace, Susan M. Sanchez, Charles J. Corrado, Lei Zhao, John R. Birge and P. A. Eibeck and has published in prestigious journals such as Journal of the American Statistical Association, European Journal of Operational Research and Operations Research.

In The Last Decade

Julia L. Higle

32 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Julia L. Higle United States 15 616 373 295 186 151 33 1.2k
Peter Kall Switzerland 11 593 1.0× 515 1.4× 155 0.5× 174 0.9× 205 1.4× 19 1.3k
Aurélie Thiele United States 9 649 1.1× 421 1.1× 366 1.2× 205 1.1× 98 0.6× 22 1.3k
Maarten H. van der Vlerk Netherlands 16 561 0.9× 320 0.9× 198 0.7× 177 1.0× 101 0.7× 47 931
Karthik Natarajan Singapore 18 727 1.2× 226 0.6× 266 0.9× 128 0.7× 83 0.5× 50 1.2k
Rüdiger Schultz Germany 25 1.1k 1.8× 706 1.9× 352 1.2× 256 1.4× 317 2.1× 68 2.1k
Cécile Murat France 12 421 0.7× 310 0.8× 111 0.4× 228 1.2× 163 1.1× 30 1.1k
Virginie Gabrel France 14 432 0.7× 330 0.9× 117 0.4× 354 1.9× 150 1.0× 25 1.3k
Willem K. Klein Haneveld Netherlands 15 411 0.7× 217 0.6× 301 1.0× 160 0.9× 65 0.4× 42 861
A. L. Soyster United States 15 1.0k 1.7× 871 2.3× 417 1.4× 478 2.6× 327 2.2× 46 2.4k
Richard M. Van Slyke United States 5 428 0.7× 330 0.9× 145 0.5× 210 1.1× 187 1.2× 6 1.1k

Countries citing papers authored by Julia L. Higle

Since Specialization
Citations

This map shows the geographic impact of Julia L. Higle's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Julia L. Higle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julia L. Higle more than expected).

Fields of papers citing papers by Julia L. Higle

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Julia L. Higle. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Julia L. Higle. The network helps show where Julia L. Higle may publish in the future.

Co-authorship network of co-authors of Julia L. Higle

This figure shows the co-authorship network connecting the top 25 collaborators of Julia L. Higle. A scholar is included among the top collaborators of Julia L. Higle based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Julia L. Higle. Julia L. Higle is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Higle, Julia L. & Lei Zhao. (2010). Adaptive and nonadaptive approaches to statistically based methods for solving stochastic linear programs: a computational investigation. Computational Optimization and Applications. 51(2). 509–532. 4 indexed citations
2.
Higle, Julia L., et al.. (2009). Stochastic scenario decomposition for multistage stochastic programs. IMA Journal of Management Mathematics. 21(1). 39–66. 7 indexed citations
3.
Higle, Julia L. & Suvrajeet Sen. (2006). Multistage stochastic convex programs: Duality and its implications. Annals of Operations Research. 142(1). 129–146. 6 indexed citations
4.
Sen, Suvrajeet & Julia L. Higle. (2005). The C3 Theorem and a D2 Algorithm for Large Scale Stochastic Mixed-Integer Programming: Set Convexification. Mathematical Programming. 104(1). 1–20. 113 indexed citations
5.
Higle, Julia L. & Stein W. Wallace. (2003). Sensitivity Analysis and Uncertainty in Linear Programming. INFORMS Journal on Applied Analytics. 33(4). 53–60. 55 indexed citations
6.
Eibeck, P. A., et al.. (2003). A collaborative Master of Engineering program. 3. 13D3/7–13D312.
7.
Sen, Suvrajeet, Julia L. Higle, & John R. Birge. (2000). Duality Gaps in Stochastic Integer Programming. Journal of Global Optimization. 18(2). 189–194. 3 indexed citations
8.
Higle, Julia L. & Suvrajeet Sen. (1996). Stochastic Decomposition : A Statistical Method for Large Scale Stochastic Linear Programming. CERN Document Server (European Organization for Nuclear Research). 127 indexed citations
9.
Higle, Julia L. & Suvrajeet Sen. (1996). Duality and statistical tests of optimality for two stage stochastic programs. Mathematical Programming. 75(2). 257–275. 31 indexed citations
10.
Higle, Julia L. & Suvrajeet Sen. (1995). Statistical approximations for recourse constrained stochastic programs. Annals of Operations Research. 56(1). 157–175. 6 indexed citations
11.
Higle, Julia L., et al.. (1994). Conditional Stochastic Decomposition: An Algorithmic Interface for Optimization and Simulation. Operations Research. 42(2). 311–322. 12 indexed citations
12.
Higle, Julia L., et al.. (1994). Inexact subgradient methods with applications in stochastic programming. Mathematical Programming. 63(1-3). 65–82. 6 indexed citations
13.
Sanchez, Susan M. & Julia L. Higle. (1992). Observational Studies of Rare Events: A Subset Selection Approach. Journal of the American Statistical Association. 87(419). 878–883. 7 indexed citations
14.
Higle, Julia L. & Suvrajeet Sen. (1992). On the Convergence of Algorithms with Implications for Stochastic and Nondifferentiable Optimization. Mathematics of Operations Research. 17(1). 112–131. 25 indexed citations
15.
Higle, Julia L.. (1991). Production planning with discounting and stochastic demands. European Journal of Operational Research. 50(3). 257–265. 3 indexed citations
16.
Higle, Julia L. & Suvrajeet Sen. (1991). Statistical verification of optimality conditions for stochastic programs with recourse. Annals of Operations Research. 30(1). 215–239. 30 indexed citations
17.
Higle, Julia L., James C. Bean, & Robert L. Smith. (1990). Deterministic Equivalence in Stochastic Infinite Horizon Problems. Mathematics of Operations Research. 15(3). 396–407. 9 indexed citations
18.
Higle, Julia L., et al.. (1989). A COMPARISON OF TECHNIQUES FOR THE IDENTIFICATION OF HAZARDOUS LOCATIONS. Transportation Research Record Journal of the Transportation Research Board. 27 indexed citations
19.
Higle, Julia L.. (1989). A Note on the Sensitivity of the EOQ. IIE Transactions. 21(3). 294–297. 2 indexed citations
20.
Higle, Julia L., et al.. (1988). BAYESIAN IDENTIFICATION OF HAZARDOUS LOCATIONS (WITH DISCUSSION AND CLOSURE). Transportation Research Record Journal of the Transportation Research Board. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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