Skip to content

SYNOPSYS INC: general and administrative expense

General and administrative expense for SYNOPSYS INC. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All SYNOPSYS INC financial histories

What this measure means

General and administrative costs reported under this concept, separate from selling expense. Filers group overhead differently, so compare presentation before comparing companies.

Exact concept: us-gaap:GeneralAndAdministrativeExpense. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2007-11-01 to 2025-10-31. The SEC response was captured on 2026-09-20.

Selected filing history

General and administrative expense in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2024-11-012025-10-31769,648,000USD2025-12-2210-K · 0000883241-25-000028
2023-11-012024-10-31568,496,000USD2025-12-2210-K · 0000883241-25-000028
2022-11-012023-10-31376,677,000USD2025-12-2210-K · 0000883241-25-000028
2021-11-012022-10-31313,554,000USD2024-12-1910-K · 0000883241-24-000024
2020-11-012021-10-31322,988,000USD2023-12-1210-K · 0000883241-23-000019
2019-11-012020-10-31284,530,000USD2022-12-1210-K · 0000883241-22-000017
2018-11-012019-10-31229,218,000USD2021-12-1310-K · 0000883241-21-000022
2017-11-012018-10-31262,560,000USD2020-12-1510-K · 0000883241-20-000015
2016-11-012017-10-31196,844,000USD2019-12-2010-K · 0000883241-19-000019
2015-11-012016-10-31165,962,000USD2018-12-1710-K · 0000883241-18-000012
2014-11-012015-10-31165,097,000USD2017-12-1410-K · 0000883241-17-000014
2013-11-012014-10-31155,215,000USD2016-12-1210-K · 0000883241-16-000027
2012-11-012013-10-31143,791,000USD2015-12-1410-K · 0000883241-15-000014
2011-11-012012-10-31157,459,000USD2014-12-1510-K · 0000883241-14-000008
2010-11-012011-10-31112,760,000USD2013-12-2010-K · 0001193125-13-479653
2009-11-012010-10-31114,887,000USD2012-12-2010-K · 0001193125-12-510596
2008-11-012009-10-31119,100,000USD2011-12-1610-K · 0001193125-11-343034
2007-11-012008-10-31103,852,000USD2011-02-0910-K/A · 0001193125-11-028105

Related financial histories

Inspect the source

Entity
SYNOPSYS INC / CIK 0000883241
Captured
2026-09-20T05:02:10.035Z
SEC response SHA-256
7e9b6e13ac2481586b56f1db93af14b9b014a1b7f92af2a09075d7cbd51526d4

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000883241.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))